From f81cbfe8d82d2e5ca1bf9e6bd9ae3f48e173e3fd Mon Sep 17 00:00:00 2001 From: profxj Date: Wed, 22 Jun 2022 11:19:21 -0700 Subject: [PATCH 001/104] updates --- zdm/craco/testing.py | 17 ++++++----------- 1 file changed, 6 insertions(+), 11 deletions(-) diff --git a/zdm/craco/testing.py b/zdm/craco/testing.py index 38143c6b..0a183b5a 100644 --- a/zdm/craco/testing.py +++ b/zdm/craco/testing.py @@ -61,21 +61,15 @@ def main(pargs): wzvals = [] # for tt, pval in enumerate(pvals): vparams[pargs.param] = pval - C,llC,lltot=it.minimise_const_only( + C,llC=it.minimise_const_only( vparams,grids,surveys, Verbose=False) # Set lC vparams['lC']=C igrid.state.FRBdemo.lC = C # Grab final LL - # TODO -- bring this back - #lls_final, nterm, pvterm, lpvals, lwz = it.calc_likelihoods_2D( - # igrid, isurvey, - # norm=True,psnr=True,dolist=4) - # TODO -- remove this - items = it.calc_likelihoods_2D( + lls_final, nterm, pvterm, lpvals, lwz = it.calc_likelihoods_2D( igrid, isurvey, - norm=True,psnr=True,dolist=5) - embed(header='78 of testing') + norm=True,psnr=True,dolist=4) # Hold lls.append(lls_final) nterms.append(nterm) @@ -137,7 +131,7 @@ def main(pargs): parser.add_argument('--nFRB',type=int,default=1000,required=False,help="number of FRBs to analyze") parser.add_argument('--iFRB',type=int,default=0,required=False,help="starting number of FRBs to analyze") parser.add_argument('-o','--opfile',type=str,required=False,help="Output file for the data") -parser.add_argument('--survey',type=str,default='CRACO_alpha1_Planck18', +parser.add_argument('--survey',type=str,default='CRACO_std_May2022', required=False,help="Survey name") parser.add_argument('--lum_func',type=int,default=0, required=False,help="Luminosity function (0=power-law, 1=gamma)") pargs = parser.parse_args() @@ -163,7 +157,6 @@ def main(pargs): # Newest round python testing.py lEmax 41. 43. --nstep 50 --nFRB 100 -o MC_Plots/CRACO_100_Emax_new.png -python testing.py H0 60. 80. --nstep 50 --nFRB 100 -o MC_Plots/CRACO_100_H0.png # Gamma python testing.py H0 60. 80. --nstep 50 --nFRB 100 --survey CRACO_alpha1_Planck18_Gamma -o MC_Plots/CRACO_100_H0_Gamma.png --lum_func 2 @@ -172,4 +165,6 @@ def main(pargs): python testing.py alpha 0. 2. --nstep 50 --nFRB 100 --survey CRACO_alpha1_Planck18_Gamma -o MC_Plots/CRACO_100_alpha_Gamma.png --lum_func 2 python testing.py sfr_n 0. 5. --nstep 100 --nFRB 100 --iFRB 100 --survey CRACO_alpha1_Planck18_Gamma -o MC_Plots/CRACO_100_sfr_Gamma.png --lum_func 2 # + +python testing.py H0 60. 80. --nstep 50 --nFRB 100 -o MC_Plots/CRACO_100_H0.png --lum_func 2 ''' \ No newline at end of file From e4a61441ef93d23cfb678c1db5a2aee35d3f197f Mon Sep 17 00:00:00 2001 From: profxj Date: Thu, 23 Jun 2022 12:39:42 -0700 Subject: [PATCH 002/104] doc --- zdm/grid.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/zdm/grid.py b/zdm/grid.py index 54a8c59f..6a6b4408 100644 --- a/zdm/grid.py +++ b/zdm/grid.py @@ -632,7 +632,7 @@ def update(self, vparams:dict, ALL=False, prev_grid=None): set_evol = True new_sfr_smear = True - # Mask? + # DM_host # IT IS IMPORTANT TO USE np.any so that each item is executed!! if np.any([self.chk_upd_param('lmean', vparams, update=True), self.chk_upd_param('lsigma', vparams, update=True)]): From 2278b9d72413b7df51ca17b8aacc05149df65f42 Mon Sep 17 00:00:00 2001 From: Jay-MBP Date: Tue, 28 Jun 2022 12:49:06 -0700 Subject: [PATCH 003/104] Added a method to create figure of p(DM, z) while varying IGM.F --- papers/H0_I/Figures/py/figs_zdm_H0_I.py | 702 +++++++++++++++--------- papers/H0_I/Figures/py/testing.py | 5 + zdm/scripts/plot_pzdm_grid.py | 138 +++-- 3 files changed, 529 insertions(+), 316 deletions(-) create mode 100644 papers/H0_I/Figures/py/testing.py diff --git a/papers/H0_I/Figures/py/figs_zdm_H0_I.py b/papers/H0_I/Figures/py/figs_zdm_H0_I.py index ffc8f657..7a33c3c1 100644 --- a/papers/H0_I/Figures/py/figs_zdm_H0_I.py +++ b/papers/H0_I/Figures/py/figs_zdm_H0_I.py @@ -12,9 +12,9 @@ import matplotlib.gridspec as gridspec from matplotlib import pyplot as plt -from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER +# from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER -mpl.rcParams['font.family'] = 'stixgeneral' +mpl.rcParams["font.family"] = "stixgeneral" import pandas import seaborn as sns @@ -34,15 +34,22 @@ sys.path.append(os.path.abspath("../../Analysis/py")) import analy_H0_I -def fig_craco_fiducial(outfile='fig_craco_fiducial.png', - zmax=2.5,DMmax=2500, - show_Macquart=False, - log=True, - label='$\\log_{10} \; p(DM_{\\rm EG},z)$', - Aconts=[0.01, 0.1, 0.5], - cmap='jet', show=False, figsize=None, - vmnx=(None,None), - grid=None, survey=None): + +def fig_craco_fiducial( + outfile="fig_craco_fiducial.png", + zmax=2.5, + DMmax=2500, + show_Macquart=False, + log=True, + label="$\\log_{10} \; p(DM_{\\rm EG},z)$", + Aconts=[0.01, 0.1, 0.5], + cmap="jet", + show=False, + figsize=None, + vmnx=(None, None), + grid=None, + survey=None, +): """ Very complicated routine for plotting 2D zdm grids @@ -72,108 +79,123 @@ def fig_craco_fiducial(outfile='fig_craco_fiducial.png', # Unpack full_zDMgrid, zvals, dmvals = grid.rates, grid.zvals, grid.dmvals - FRBZ=survey.frbs['Z'] - FRBDM=survey.DMEGs - + FRBZ = survey.frbs["Z"] + FRBDM = survey.DMEGs + ##### imshow of grid ####### - fsize = 14. + fsize = 14.0 plt.figure(figsize=figsize) - ax1=plt.axes() + ax1 = plt.axes() plt.sca(ax1) - - plt.xlabel('z') - plt.ylabel('${\\rm DM}_{\\rm EG}$') - #plt.title(title+str(H0)) - + + plt.xlabel("z") + plt.ylabel("${\\rm DM}_{\\rm EG}$") + # plt.title(title+str(H0)) + # Cut down grid zvals, dmvals, zDMgrid = figures.proc_pgrid( - full_zDMgrid, - zvals, (0, zmax), - dmvals, (0, DMmax)) - ddm=dmvals[1]-dmvals[0] - dz=zvals[1]-zvals[0] + full_zDMgrid, zvals, (0, zmax), dmvals, (0, DMmax) + ) + ddm = dmvals[1] - dmvals[0] + dz = zvals[1] - zvals[0] nz, ndm = zDMgrid.shape # Contours alevels = figures.find_Alevels(full_zDMgrid, Aconts, log=True) - + # Ticks - tvals, ticks = figures.ticks_pgrid(zvals)# , fmt='str4') + tvals, ticks = figures.ticks_pgrid(zvals) # , fmt='str4') plt.xticks(tvals, ticks) - tvals, ticks = figures.ticks_pgrid(dmvals, fmt='int')# , fmt='str4') + tvals, ticks = figures.ticks_pgrid(dmvals, fmt="int") # , fmt='str4') plt.yticks(tvals, ticks) - # Image - im=plt.imshow(zDMgrid.T,cmap=cmap,origin='lower', - vmin=vmnx[0], vmax=vmnx[1], - interpolation='None', - aspect='auto') - - styles=['--','-.',':'] - ax=plt.gca() - cs=ax.contour(zDMgrid.T,levels=alevels,origin='lower',colors="white",linestyles=styles) + # Image + im = plt.imshow( + zDMgrid.T, + cmap=cmap, + origin="lower", + vmin=vmnx[0], + vmax=vmnx[1], + interpolation="None", + aspect="auto", + ) + + styles = ["--", "-.", ":"] + ax = plt.gca() + cs = ax.contour( + zDMgrid.T, levels=alevels, origin="lower", colors="white", linestyles=styles + ) - ax=plt.gca() - - muDMhost=np.log(10**grid.state.host.lmean) - sigmaDMhost=np.log(10**grid.state.host.lsigma) - meanHost = np.exp(muDMhost + sigmaDMhost**2/2.) - medianHost = np.exp(muDMhost) + ax = plt.gca() + + muDMhost = np.log(10 ** grid.state.host.lmean) + sigmaDMhost = np.log(10 ** grid.state.host.lsigma) + meanHost = np.exp(muDMhost + sigmaDMhost ** 2 / 2.0) + medianHost = np.exp(muDMhost) print(f"Host: mean={meanHost}, median={medianHost}") - plt.ylim(0,ndm-1) - plt.xlim(0,nz-1) - zmax=zvals[-1] - nz=zvals.size - #DMbar, zeval = igm.average_DM(zmax, cumul=True, neval=nz+1) + plt.ylim(0, ndm - 1) + plt.xlim(0, nz - 1) + zmax = zvals[-1] + nz = zvals.size + # DMbar, zeval = igm.average_DM(zmax, cumul=True, neval=nz+1) DM_cosmic = pcosmic.get_mean_DM(zvals, grid.state) - - #idea is that 1 point is 1, hence... - zeval = zvals/dz - DMEG_mean = (DM_cosmic+meanHost)/ddm - DMEG_median = (DM_cosmic+medianHost)/ddm + # idea is that 1 point is 1, hence... + zeval = zvals / dz + DMEG_mean = (DM_cosmic + meanHost) / ddm + DMEG_median = (DM_cosmic + medianHost) / ddm # Check median f_median = scipy.interpolate.interp1d( - zvals, DM_cosmic+medianHost, - fill_value='extrapolate') + zvals, DM_cosmic + medianHost, fill_value="extrapolate" + ) eval_DMEG = f_median(FRBZ) above = FRBDM > eval_DMEG print(f"There are {np.sum(above)/len(FRBZ)} above the median") if show_Macquart: - plt.plot(zeval,DMEG_mean,color='gray',linewidth=2, - label='Macquart relation (mean)') - plt.plot(zeval,DMEG_median,color='gray', - linewidth=2, ls='--', - label='Macquart relation (median)') - l=plt.legend(loc='lower right',fontsize=12) - #l=plt.legend(bbox_to_anchor=(0.2, 0.8),fontsize=8) - #for text in l.get_texts(): - # text.set_color("white") - + plt.plot( + zeval, + DMEG_mean, + color="gray", + linewidth=2, + label="Macquart relation (mean)", + ) + plt.plot( + zeval, + DMEG_median, + color="gray", + linewidth=2, + ls="--", + label="Macquart relation (median)", + ) + l = plt.legend(loc="lower right", fontsize=12) + # l=plt.legend(bbox_to_anchor=(0.2, 0.8),fontsize=8) + # for text in l.get_texts(): + # text.set_color("white") + # limit to a reasonable range if logscale if log and vmnx[0] is None: - themax=zDMgrid.max() - themin=int(themax-4) - themax=int(themax) - plt.clim(themin,themax) - + themax = zDMgrid.max() + themin = int(themax - 4) + themax = int(themax) + plt.clim(themin, themax) + ##### add FRB host galaxies at some DM/redshift ##### if FRBZ is not None: - iDMs=FRBDM/ddm - iZ=FRBZ/dz + iDMs = FRBDM / ddm + iZ = FRBZ / dz # Restrict to plot range gd = (FRBDM < DMmax) & (FRBZ < zmax) - plt.plot(iZ[gd],iDMs[gd],'ko',linestyle="",markersize=2.) + plt.plot(iZ[gd], iDMs[gd], "ko", linestyle="", markersize=2.0) - cbar=plt.colorbar(im,fraction=0.046, shrink=1.2,aspect=15,pad=0.05) + cbar = plt.colorbar(im, fraction=0.046, shrink=1.2, aspect=15, pad=0.05) cbar.set_label(label) fig_utils.set_fontsize(ax, fsize) - + plt.tight_layout() - + if show: plt.show() else: @@ -182,10 +204,15 @@ def fig_craco_fiducial(outfile='fig_craco_fiducial.png', plt.close() -def fig_craco_varyH0_zDM(outfile, - zmax=2.3,DMmax=1500, - norm=2, other_param='Emax', - Aconts=[0.05], fuss_with_ticks:bool=False): +def fig_craco_varyH0_zDM( + outfile, + zmax=2.3, + DMmax=1500, + norm=2, + other_param="Emax", + Aconts=[0.05], + fuss_with_ticks: bool = False, +): """_summary_ Args: @@ -199,115 +226,116 @@ def fig_craco_varyH0_zDM(outfile, """ # Generate the grid survey, grid = analy_H0_I.craco_mc_survey_grid() - #survey, grid = loading.survey_and_grid( + # survey, grid = loading.survey_and_grid( # survey_name='CRACO_alpha1_Planck18_Gamma', # NFRB=100, lum_func=2) fiducial_Emax = grid.state.energy.lEmax fiducial_F = grid.state.IGM.F plt.figure() - ax1=plt.axes() + ax1 = plt.axes() plt.sca(ax1) - - plt.xlabel('z') - plt.ylabel('${\\rm DM}_{\\rm EG}$') - #plt.title(title+str(H0)) - - if other_param == 'Emax': - H0_values = [60., 70., 80., 80.] - other_values = [0., 0., 0., -0.1] - lstyles = ['-', '-', '-', ':'] - zticks = [0.5, 1.0, 1.5, 2.] - ylim = (0., DMmax) - elif other_param == 'F': - H0_values = [60., 70., 80., 60.] + + plt.xlabel("z") + plt.ylabel("${\\rm DM}_{\\rm EG}$") + # plt.title(title+str(H0)) + + if other_param == "Emax": + H0_values = [60.0, 70.0, 80.0, 80.0] + other_values = [0.0, 0.0, 0.0, -0.1] + lstyles = ["-", "-", "-", ":"] + zticks = [0.5, 1.0, 1.5, 2.0] + ylim = (0.0, DMmax) + elif other_param == "F": + H0_values = [60.0, 70.0, 80.0, 60.0] other_values = [fiducial_F, fiducial_F, fiducial_F, 0.5] - lstyle = '-' + lstyle = "-" zticks, ylim = None, None # Loop on grids legend_lines = [] labels = [] for H0, scl, lstyle, clr in zip( - H0_values, - other_values, - lstyles, - ['b', 'k','r', 'gray']): + H0_values, other_values, lstyles, ["b", "k", "r", "gray"] + ): # Update grid vparams = {} - vparams['H0'] = H0 - if other_param == 'Emax': - vparams['lEmax'] = fiducial_Emax + scl - elif other_param == 'F': - vparams['F'] = scl + vparams["H0"] = H0 + if other_param == "Emax": + vparams["lEmax"] = fiducial_Emax + scl + elif other_param == "F": + vparams["F"] = scl grid.update(vparams) # Unpack - full_zDMgrid, zvals, dmvals = grid.rates.copy(), grid.zvals.copy(), grid.dmvals.copy() - + full_zDMgrid, zvals, dmvals = ( + grid.rates.copy(), + grid.zvals.copy(), + grid.dmvals.copy(), + ) + # currently this is "per cell" - now to change to "per DM" # normalises the grid by the bin width, i.e. probability per bin, not probability density - + # checks against zeros for a log-plot zvals, dmvals, zDMgrid = figures.proc_pgrid( - full_zDMgrid, - zvals, (0, zmax), - dmvals, (0, DMmax)) + full_zDMgrid, zvals, (0, zmax), dmvals, (0, DMmax) + ) # Contours alevels = figures.find_Alevels(full_zDMgrid, Aconts) - - # sets the x and y tics + + # sets the x and y tics # JXP fussing here!! - tvals, ticks = figures.ticks_pgrid(zvals, these_vals=zticks)# , fmt='str4') + tvals, ticks = figures.ticks_pgrid(zvals, these_vals=zticks) # , fmt='str4') plt.xticks(tvals, ticks) - tvals, ticks = figures.ticks_pgrid(dmvals, fmt='int')# , fmt='str4') + tvals, ticks = figures.ticks_pgrid(dmvals, fmt="int") # , fmt='str4') plt.yticks(tvals, ticks) - ax=plt.gca() - cs=ax.contour(zDMgrid.T,levels=alevels, - origin='lower',colors=[clr], - linestyles=lstyle) + ax = plt.gca() + cs = ax.contour( + zDMgrid.T, levels=alevels, origin="lower", colors=[clr], linestyles=lstyle + ) leg, _ = cs.legend_elements() legend_lines.append(leg[0]) # Label - if other_param == 'Emax': - labels.append(r"$H_0 = $"+f"{H0}, log "+r"$E_{\rm max}$"+f"= {vparams['lEmax']}") - elif other_param == 'F': - labels.append(r"$H_0 = $"+f"{H0}, F = {vparams['F']}") + if other_param == "Emax": + labels.append( + r"$H_0 = $" + f"{H0}, log " + r"$E_{\rm max}$" + f"= {vparams['lEmax']}" + ) + elif other_param == "F": + labels.append(r"$H_0 = $" + f"{H0}, F = {vparams['F']}") ###### gets decent axis labels, down to 1 decimal place ####### - ax=plt.gca() - ax.legend(legend_lines, labels, loc='lower right') + ax = plt.gca() + ax.legend(legend_lines, labels, loc="lower right") # Fontsize - fig_utils.set_fontsize(ax, 16.) + fig_utils.set_fontsize(ax, 16.0) # Axis limits - #if xlim is not None: + # if xlim is not None: # ax.set_xlim(xlim[0], xlim[1]) - #if ylim is not None: + # if ylim is not None: # ax.set_ylim(ylim[0], ylim[1]) # Ticks if fuss_with_ticks: labels = [item.get_text() for item in ax.get_xticklabels()] for i in np.arange(len(labels)): - labels[i]=labels[i][0:4] + labels[i] = labels[i][0:4] ax.set_xticklabels(labels) labels = [item.get_text() for item in ax.get_yticklabels()] for i in np.arange(len(labels)): - if '.' in labels[i]: - labels[i]=labels[i].split('.')[0] + if "." in labels[i]: + labels[i] = labels[i].split(".")[0] ax.set_yticklabels(labels) ax.yaxis.labelpad = 0 - - # Finish plt.tight_layout() plt.savefig(outfile, dpi=300) @@ -315,117 +343,124 @@ def fig_craco_varyH0_zDM(outfile, print(f"Wrote: {outfile}") -def fig_craco_varyH0_other(outfile, params, - zmax=2,DMmax=1500, - smax=25.*9, - other_param='Emax', - Aconts=[0.05], debug:bool=False): +def fig_craco_varyH0_other( + outfile, + params, + zmax=2, + DMmax=1500, + smax=25.0 * 9, + other_param="Emax", + Aconts=[0.05], + debug: bool = False, +): - if other_param == 'Emax': - H0_values = [60., 70., 80., 80.] + if other_param == "Emax": + H0_values = [60.0, 70.0, 80.0, 80.0] other_values = [41.4, 41.4, 41.4, 41.3] - lstyles = ['-', '-', '-', ':'] - elif other_param == 'F': - H0_values = [60., 70., 80., 60.] + lstyles = ["-", "-", "-", ":"] + elif other_param == "F": + H0_values = [60.0, 70.0, 80.0, 60.0] other_values = [fiducial_F, fiducial_F, fiducial_F, 0.5] - lstyle = '-' + lstyle = "-" plt.figure() - ax1=plt.axes() + ax1 = plt.axes() plt.sca(ax1) - - if params == 'sDM': - plt.xlabel(r'DM$_{\rm EG}$') + + if params == "sDM": + plt.xlabel(r"DM$_{\rm EG}$") else: - plt.xlabel(r'$z$') - #plt.ylabel(r'$s$') - plt.ylabel(r'SNR') + plt.xlabel(r"$z$") + # plt.ylabel(r'$s$') + plt.ylabel(r"SNR") # Loop on grids legend_lines = [] labels = [] first = True for H0, lEmax, lstyle, clr in zip( - H0_values, - other_values, - lstyles, - ['b', 'k','r', 'gray']): + H0_values, other_values, lstyles, ["b", "k", "r", "gray"] + ): # Unpack - grid_file = f'../Analysis/GridData/p{params}_H0{int(H0)}_Emax{lEmax}.npz' + grid_file = f"../Analysis/GridData/p{params}_H0{int(H0)}_Emax{lEmax}.npz" print(f"Loading: {grid_file}") data = np.load(grid_file) - if params == 'sDM': - full_pgrid = data['psDM'] - dmvals = data['dmvals'] + if params == "sDM": + full_pgrid = data["psDM"] + dmvals = data["dmvals"] else: - full_pgrid = data['psz'] - zvals = data['zvals'] - snrs = data['snrs'] - + full_pgrid = data["psz"] + zvals = data["zvals"] + snrs = data["snrs"] + # Process full grid - if params == 'sDM': - snrs, dmvals, cut_pgrid = figures.proc_pgrid(full_pgrid, - snrs[0:-1], (0, smax), - dmvals, (0, DMmax)) + if params == "sDM": + snrs, dmvals, cut_pgrid = figures.proc_pgrid( + full_pgrid, snrs[0:-1], (0, smax), dmvals, (0, DMmax) + ) else: - snrs, zvals, cut_pgrid = figures.proc_pgrid(full_pgrid, - 9*snrs[0:-1], (0, smax), - zvals, (0, zmax)) + snrs, zvals, cut_pgrid = figures.proc_pgrid( + full_pgrid, 9 * snrs[0:-1], (0, smax), zvals, (0, zmax) + ) # Contours alevels = figures.find_Alevels(full_pgrid, Aconts) if first: if debug: - im=plt.imshow(cut_pgrid,cmap='jet',origin='lower', - interpolation='None', + im = plt.imshow( + cut_pgrid, + cmap="jet", + origin="lower", + interpolation="None", # extent=[0., 2, 0, 2000.], - vmin=-30., - aspect='auto') - - # sets the x and y tics - if params == 'sz': - tvals, ticks = figures.ticks_pgrid(zvals)# , fmt='str4') + vmin=-30.0, + aspect="auto", + ) + + # sets the x and y tics + if params == "sz": + tvals, ticks = figures.ticks_pgrid(zvals) # , fmt='str4') else: - tvals, ticks = figures.ticks_pgrid(dmvals, fmt='int') + tvals, ticks = figures.ticks_pgrid(dmvals, fmt="int") plt.xticks(tvals, ticks) - tvals, ticks = figures.ticks_pgrid(snrs, fmt='str4') + tvals, ticks = figures.ticks_pgrid(snrs, fmt="str4") plt.yticks(tvals, ticks) # first = False - ax=plt.gca() - cs=ax.contour(cut_pgrid,levels=alevels, - origin='lower',colors=[clr], - linestyles=lstyle) + ax = plt.gca() + cs = ax.contour( + cut_pgrid, levels=alevels, origin="lower", colors=[clr], linestyles=lstyle + ) leg, _ = cs.legend_elements() legend_lines.append(leg[0]) # Label - if other_param == 'Emax': - labels.append(r"$H_0 = $"+f"{H0}, log "+r"$E_{\rm max}$"+f"= {lEmax}") - elif other_param == 'F': - labels.append(r"$H_0 = $"+f"{H0}, F = {vparams['F']}") + if other_param == "Emax": + labels.append(r"$H_0 = $" + f"{H0}, log " + r"$E_{\rm max}$" + f"= {lEmax}") + elif other_param == "F": + labels.append(r"$H_0 = $" + f"{H0}, F = {vparams['F']}") ###### gets decent axis labels, down to 1 decimal place ####### - ax=plt.gca() - ax.legend(legend_lines, labels, loc='upper right') + ax = plt.gca() + ax.legend(legend_lines, labels, loc="upper right") # Ticks labels = [item.get_text() for item in ax.get_xticklabels()] for i in np.arange(len(labels)): - labels[i]=labels[i][0:4] + labels[i] = labels[i][0:4] ax.set_xticklabels(labels) labels = [item.get_text() for item in ax.get_yticklabels()] for i in np.arange(len(labels)): - if '.' in labels[i]: - labels[i]=labels[i].split('.')[0] + if "." in labels[i]: + labels[i] = labels[i].split(".")[0] ax.set_yticklabels(labels) ax.yaxis.labelpad = 0 - - fig_utils.set_fontsize(ax, 15.) + + fig_utils.set_fontsize(ax, 15.0) # Finish plt.tight_layout() @@ -433,18 +468,19 @@ def fig_craco_varyH0_other(outfile, params, plt.close() print(f"Wrote: {outfile}") -def fig_craco_H0vsEmax(outfile='fig_craco_H0vsEmax.png'): + +def fig_craco_H0vsEmax(outfile="fig_craco_H0vsEmax.png"): # Load the cube - cube_out = np.load('../Analysis/Cubes/craco_H0_Emax_cube.npz') - ll = cube_out['ll'] # log10 + cube_out = np.load("../Analysis/Cubes/craco_H0_Emax_cube.npz") + ll = cube_out["ll"] # log10 # Slurp - lEmax = cube_out['lEmax'] - H0 = cube_out['H0'] + lEmax = cube_out["lEmax"] + H0 = cube_out["H0"] # - dE = lEmax[1]-lEmax[0] + dE = lEmax[1] - lEmax[0] dH = H0[1] - H0[0] - + # Normalize ll -= ll.max() @@ -452,33 +488,37 @@ def fig_craco_H0vsEmax(outfile='fig_craco_H0vsEmax.png'): plt.clf() ax = plt.gca() - im=plt.imshow(ll.T,cmap='jet',origin='lower', - interpolation='None', extent=[40.4-dE/2, 43.4+dE/2, - 60.-dH/2, 80+dH/2], - aspect='auto', vmin=-4. - )#aspect=aspect) + im = plt.imshow( + ll.T, + cmap="jet", + origin="lower", + interpolation="None", + extent=[40.4 - dE / 2, 43.4 + dE / 2, 60.0 - dH / 2, 80 + dH / 2], + aspect="auto", + vmin=-4.0, + ) # aspect=aspect) # Color bar - cbar=plt.colorbar(im,fraction=0.046, shrink=1.2,aspect=15,pad=0.05) - cbar.set_label(r'$\Delta$ Log10 Likelihood') + cbar = plt.colorbar(im, fraction=0.046, shrink=1.2, aspect=15, pad=0.05) + cbar.set_label(r"$\Delta$ Log10 Likelihood") # - ax.set_xlabel('log Emax') - ax.set_ylabel('H0 (km/s/Mpc)') + ax.set_xlabel("log Emax") + ax.set_ylabel("H0 (km/s/Mpc)") plt.savefig(outfile, dpi=200) print(f"Wrote: {outfile}") -def fig_craco_H0vsF(outfile='fig_craco_H0vsF.png'): +def fig_craco_H0vsF(outfile="fig_craco_H0vsF.png"): # Load the cube - cube_out = np.load('../Analysis/Cubes/craco_H0_F_cube.npz') - ll = cube_out['ll'] # log10 + cube_out = np.load("../Analysis/Cubes/craco_H0_F_cube.npz") + ll = cube_out["ll"] # log10 # Slurp - F = cube_out['F'] - H0 = cube_out['H0'] + F = cube_out["F"] + H0 = cube_out["H0"] # - dF = F[1]-F[0] + dF = F[1] - F[0] dH = H0[1] - H0[0] - + # Normalize ll -= ll.max() @@ -486,76 +526,212 @@ def fig_craco_H0vsF(outfile='fig_craco_H0vsF.png'): plt.clf() ax = plt.gca() - im=plt.imshow(ll.T,cmap='jet',origin='lower', - interpolation='None', extent=[0.1-dF/2, 0.5+dF/2, - 60.-dH/2, 80+dH/2], - aspect='auto', vmin=-4. - )#aspect=aspect) + im = plt.imshow( + ll.T, + cmap="jet", + origin="lower", + interpolation="None", + extent=[0.1 - dF / 2, 0.5 + dF / 2, 60.0 - dH / 2, 80 + dH / 2], + aspect="auto", + vmin=-4.0, + ) # aspect=aspect) # Color bar - cbar=plt.colorbar(im,fraction=0.046, shrink=1.2,aspect=15,pad=0.05) - cbar.set_label(r'$\Delta$ Log10 Likelihood') + cbar = plt.colorbar(im, fraction=0.046, shrink=1.2, aspect=15, pad=0.05) + cbar.set_label(r"$\Delta$ Log10 Likelihood") # - ax.set_xlabel('F') - ax.set_ylabel('H0 (km/s/Mpc)') + ax.set_xlabel("F") + ax.set_ylabel("H0 (km/s/Mpc)") plt.savefig(outfile, dpi=200) print(f"Wrote: {outfile}") -def fig_fd_vs_z(outfile='fig_fd_vs_z.png'): + +def fig_fd_vs_z(outfile="fig_fd_vs_z.png"): # Redshifts - z = np.linspace(0., 2., 20) + z = np.linspace(0.0, 2.0, 20) f_d = figm.f_diffuse(z) # Plot plt.clf() ax = plt.gca() - ax.plot(z, f_d, 'k') + ax.plot(z, f_d, "k") # - ax.set_ylim(0., 1.) - ax.set_xlabel(r'$z$') - ax.set_ylabel(r'$f_d(z)$') + ax.set_ylim(0.0, 1.0) + ax.set_xlabel(r"$z$") + ax.set_ylabel(r"$f_d(z)$") ax.xaxis.set_major_locator(plt.MultipleLocator(0.5)) - fig_utils.set_fontsize(ax, 17.) + fig_utils.set_fontsize(ax, 17.0) plt.savefig(outfile, dpi=200) print(f"Wrote: {outfile}") + +def fig_craco_varyF_zDM( + outfile, + zmax=2.3, + DMmax=1500, + norm=2, + other_param="Emax", + Aconts=[0.05], + fuss_with_ticks: bool = False, +): + """_summary_ + + Args: + outfile (_type_): _description_ + zmax (float, optional): _description_. Defaults to 2.3. + DMmax (int, optional): _description_. Defaults to 1500. + norm (int, optional): _description_. Defaults to 2. + other_param (str, optional): _description_. Defaults to 'Emax'. + Aconts (list, optional): _description_. Defaults to [0.05]. + fuss_with_ticks (bool, optional): _description_. Defaults to False. + """ + # Generate the grid + survey, grid = analy_H0_I.craco_mc_survey_grid() + + # survey, grid = loading.survey_and_grid( + # survey_name='CRACO_alpha1_Planck18_Gamma', + # NFRB=100, lum_func=2) + + fiducial_Emax = grid.state.energy.lEmax + fiducial_H0 = grid.state.cosmo.H0 + + plt.figure() + ax1 = plt.axes() + + plt.sca(ax1) + + plt.xlabel("z") + plt.ylabel("${\\rm DM}_{\\rm EG}$") + + if other_param == "Emax": + F_values = [0.01, 0.3, 0.7, 0.9] + other_values = [0.0, 0.0, 0.0, -0.1] + lstyles = ["-", "-", "-", ":"] + zticks = [0.5, 1.0, 1.5, 2.0] + ylim = (0.0, DMmax) + elif other_param == "H0": + F_values = [0.01, 0.3, 0.7, 0.9] + other_values = [fiducial_H0, fiducial_H0, fiducial_H0, fiducial_H0] + lstyles = ["-", "-", "-", ":"] + zticks, ylim = None, None + + # Loop on grids + legend_lines = [] + labels = [] + for F, scl, lstyle, clr in zip( + F_values, other_values, lstyles, ["b", "k", "r", "gray"] + ): + + # Update grid + vparams = {} + vparams["F"] = F + if other_param == "Emax": + vparams["lEmax"] = fiducial_Emax + scl + elif other_param == "H0": + vparams["H0"] = scl + grid.update(vparams) + + # Unpack + full_zDMgrid, zvals, dmvals = ( + grid.rates.copy(), + grid.zvals.copy(), + grid.dmvals.copy(), + ) + + # currently this is "per cell" - now to change to "per DM" + # normalises the grid by the bin width, i.e. probability per bin, not probability density + + # checks against zeros for a log-plot + + zvals, dmvals, zDMgrid = figures.proc_pgrid( + full_zDMgrid, zvals, (0, zmax), dmvals, (0, DMmax) + ) + + # Contours + alevels = figures.find_Alevels(full_zDMgrid, Aconts) + + # sets the x and y tics + # JXP fussing here!! + tvals, ticks = figures.ticks_pgrid(zvals, these_vals=zticks) # , fmt='str4') + plt.xticks(tvals, ticks) + tvals, ticks = figures.ticks_pgrid(dmvals, fmt="int") # , fmt='str4') + plt.yticks(tvals, ticks) + + ax = plt.gca() + cs = ax.contour( + zDMgrid.T, levels=alevels, origin="lower", colors=[clr], linestyles=lstyle + ) + leg, _ = cs.legend_elements() + legend_lines.append(leg[0]) + + # Label + if other_param == "Emax": + labels.append( + r"$F = $" + f"{F}, log " + r"$E_{\rm max}$" + f"= {vparams['lEmax']}" + ) + elif other_param == "H0": + labels.append(r"$F = $" + f"{F}, H0 = {vparams['H0']}") + + ###### gets decent axis labels, down to 1 decimal place ####### + ax = plt.gca() + ax.legend(legend_lines, labels, loc="lower right") + + # Fontsize + fig_utils.set_fontsize(ax, 16.0) + + # Ticks + if fuss_with_ticks: + labels = [item.get_text() for item in ax.get_xticklabels()] + for i in np.arange(len(labels)): + labels[i] = labels[i][0:4] + ax.set_xticklabels(labels) + labels = [item.get_text() for item in ax.get_yticklabels()] + for i in np.arange(len(labels)): + if "." in labels[i]: + labels[i] = labels[i].split(".")[0] + ax.set_yticklabels(labels) + ax.yaxis.labelpad = 0 + + # Finish + plt.tight_layout() + plt.savefig(outfile, dpi=300) + plt.close() + print(f"Wrote: {outfile}") + + #### ########################## ######################### def main(pargs): # Fiducial CRACO - if pargs.figure == 'fiducial': - fig_craco_fiducial(cmap='cubehelix') + if pargs.figure == "fiducial": + fig_craco_fiducial(cmap="cubehelix") # Vary H0, Emax - if pargs.figure == 'varyH0E_zDM': - fig_craco_varyH0_zDM(outfile='fig_craco_varyH0E_zDM.png', - other_param='Emax') - if pargs.figure == 'varyH0E_sz': - fig_craco_varyH0_other('fig_craco_varyH0E_sz.png', - 'sz', other_param='Emax') - if pargs.figure == 'varyH0E_sDM': - fig_craco_varyH0_other('fig_craco_varyH0E_sDM.png', - 'sDM', other_param='Emax', DMmax=2000.) - + if pargs.figure == "varyH0E_zDM": + fig_craco_varyH0_zDM(outfile="fig_craco_varyH0E_zDM.png", other_param="Emax") + if pargs.figure == "varyH0E_sz": + fig_craco_varyH0_other("fig_craco_varyH0E_sz.png", "sz", other_param="Emax") + if pargs.figure == "varyH0E_sDM": + fig_craco_varyH0_other( + "fig_craco_varyH0E_sDM.png", "sDM", other_param="Emax", DMmax=2000.0 + ) # Vary H0, F - if pargs.figure == 'varyH0F': - fig_craco_varyH0_zDM(outfile='fig_craco_varyH0F.png', - other_param='F') - + if pargs.figure == "varyH0F": + fig_craco_varyH0_zDM(outfile="fig_craco_varyH0F.png", other_param="F") # H0 vs. Emax - if pargs.figure == 'H0vsEmax': + if pargs.figure == "H0vsEmax": fig_craco_H0vsEmax() # H0 vs. F - if pargs.figure == 'H0vsF': + if pargs.figure == "H0vsF": fig_craco_H0vsF() # f_d vs. z - if pargs.figure == 'fd_vs_z': + if pargs.figure == "fd_vs_z": fig_fd_vs_z() @@ -567,17 +743,21 @@ def parse_option(): args: (dict) dictionary of the arguments. """ parser = argparse.ArgumentParser("zdm H0 I Figures") - parser.add_argument("figure", type=str, - help="function to execute: ('fiducial, 'varyH0', 'H0vsEmax')") - #parser.add_argument('--cmap', type=str, help="Color map") - #parser.add_argument('--distr', type=str, default='normal', + parser.add_argument( + "figure", + type=str, + help="function to execute: ('fiducial, 'varyH0', 'H0vsEmax')", + ) + # parser.add_argument('--cmap', type=str, help="Color map") + # parser.add_argument('--distr', type=str, default='normal', # help='Distribution to fit [normal, lognorm]') args = parser.parse_args() - + return args + # Command line execution -if __name__ == '__main__': +if __name__ == "__main__": pargs = parse_option() main(pargs) @@ -590,4 +770,4 @@ def parse_option(): # python py/figs_zdm_H0_I.py varyH0F # python py/figs_zdm_H0_I.py varyH0E_sz # python py/figs_zdm_H0_I.py varyH0E_sDM -# python py/figs_zdm_H0_I.py fd_vs_z \ No newline at end of file +# python py/figs_zdm_H0_I.py fd_vs_z diff --git a/papers/H0_I/Figures/py/testing.py b/papers/H0_I/Figures/py/testing.py new file mode 100644 index 00000000..42596782 --- /dev/null +++ b/papers/H0_I/Figures/py/testing.py @@ -0,0 +1,5 @@ +from figs_zdm_H0_I import fig_craco_varyF_zDM, fig_craco_varyH0_zDM + +fig_craco_varyF_zDM("test.pdf", other_param="H0") +# fig_craco_varyH0_zDM("test.pdf", other_param="F") + diff --git a/zdm/scripts/plot_pzdm_grid.py b/zdm/scripts/plot_pzdm_grid.py index 4af00973..ba2a6b82 100644 --- a/zdm/scripts/plot_pzdm_grid.py +++ b/zdm/scripts/plot_pzdm_grid.py @@ -19,65 +19,71 @@ from zdm import survey from matplotlib import pyplot as plt + def main(): - + # in case you wish to switch to another output directory - opdir='Localised_FRBs/' + opdir = "Localised_FRBs/" if not os.path.exists(opdir): os.mkdir(opdir) - + # Initialise surveys and grids - + # The below is for private, unpublished FRBs. You will NOT see this in the repository! - names = ['private_CRAFT_ICS','private_CRAFT_ICS_892','private_CRAFT_ICS_1632'] - sdir='../data/Surveys/' - + # names = ['private_CRAFT_ICS','private_CRAFT_ICS_892','private_CRAFT_ICS_1632'] + sdir = "../data/Surveys/" + # Public CRAFT FRBs - #names = ['CRAFT_ICS','CRAFT_ICS_892','CRAFT_ICS_1632'] - - #Examples for other FRB surveys - #names = ['FAST','Arecibo','parkes_mb_class_I_and_II'] - + # names = ["CRAFT_ICS", "CRAFT_ICS_892", "CRAFT_ICS_1632"] + + # Examples for other FRB surveys + # names = ["FAST", "Arecibo", "parkes_mb_class_I_and_II"] + + names = ["parkes_mb_class_I_and_II"] + # if True, this generates a summed histogram of all the surveys, weighted by # the observation time - sumit=True - + sumit = True + # approximate best-fit values from recent analysis vparams = {} - vparams['H0'] = 73 - vparams['lEmax'] = 41.3 - vparams['gamma'] = -0.9 - vparams['alpha'] = 1 - vparams['sfr_n'] = 1.15 - vparams['lmean'] = 2.25 - vparams['lsigma'] = 0.55 - - zvals=[] - dmvals=[] - grids=[] - surveys=[] - nozlist=[] - for i,name in enumerate(names): - s,g = loading.survey_and_grid( - survey_name=name,NFRB=None,sdir=sdir) # should be equal to actual number of FRBs, but for this purpose it doesn't matter + vparams["H0"] = 73 + vparams["lEmax"] = 41.3 + vparams["gamma"] = -0.9 + vparams["alpha"] = 1 + vparams["sfr_n"] = 1.15 + vparams["lmean"] = 2.25 + vparams["lsigma"] = 0.55 + + zvals = [] + dmvals = [] + grids = [] + surveys = [] + nozlist = [] + for i, name in enumerate(names): + s, g = loading.survey_and_grid( + survey_name=name, NFRB=10, sdir=sdir + ) # should be equal to actual number of FRBs, but for this purpose it doesn't matter grids.append(g) surveys.append(s) - + + print("[TEST] s_type: ", s.TOBS) + # set up new parameters g.update(vparams) - + # gets cumulative rate distribution - if i==0: - rtot = np.copy(g.rates)*s.TOBS + if i == 0: + rtot = np.copy(g.rates) * s.TOBS else: - rtot += g.rates*s.TOBS - - if name=='Arecibo': + rtot += g.rates * s.TOBS + + if name == "Arecibo": # remove high DM vals from rates as per ALFA survey limit - delete=np.where(g.dmvals > 2038)[0] - g.rates[:,delete]=0. - - print(i,name,s.frbs["Z"]) + delete = np.where(g.dmvals > 2038)[0] + g.rates[:, delete] = 0.0 + + print(i, name, s.frbs["Z"]) for iFRB in s.zlist: zvals.append(s.Zs[iFRB]) dmvals.append(s.DMEGs[iFRB]) @@ -85,21 +91,43 @@ def main(): nozlist.append(dm) print("About to plot") ############# do 2D plots ########## - misc_functions.plot_grid_2(g.rates,g.zvals,g.dmvals, - name=opdir+name+'.pdf',norm=3,log=True,label='$\\log_{10} p({\\rm DM}_{\\rm EG},z)$ [a.u.]', - project=False,FRBDM=s.DMEGs,FRBZ=s.frbs["Z"],Aconts=[0.01,0.1,0.5],zmax=1.5, - DMmax=1500)#,DMlines=s.DMEGs[s.nozlist]) - + misc_functions.plot_grid_2( + g.rates, + g.zvals, + g.dmvals, + name=opdir + name + ".pdf", + norm=3, + log=True, + label="$\\log_{10} p({\\rm DM}_{\\rm EG},z)$ [a.u.]", + project=False, + FRBDM=s.DMEGs, + FRBZ=s.frbs["Z"], + Aconts=[0.01, 0.1, 0.5], + zmax=1.5, + DMmax=1500, + ) # ,DMlines=s.DMEGs[s.nozlist]) + if sumit: # does the final plot of all data - frbzvals=np.array(zvals) - frbdmvals=np.array(dmvals) + frbzvals = np.array(zvals) + frbdmvals = np.array(dmvals) ############# do 2D plots ########## - misc_functions.plot_grid_2(g.rates,g.zvals,g.dmvals, - name=opdir+'combined_localised_FRBs.pdf',norm=3,log=True, - label='$\\log_{10} p({\\rm DM}_{\\rm EG},z)$ [a.u.]', - project=False,FRBDM=frbdmvals,FRBZ=frbzvals,Aconts=[0.01,0.1,0.5], - zmax=1.5,DMmax=2000,DMlines=nozlist) - - + misc_functions.plot_grid_2( + g.rates, + g.zvals, + g.dmvals, + name=opdir + "combined_localised_FRBs.pdf", + norm=3, + log=True, + label="$\\log_{10} p({\\rm DM}_{\\rm EG},z)$ [a.u.]", + project=False, + FRBDM=frbdmvals, + FRBZ=frbzvals, + Aconts=[0.01, 0.1, 0.5], + zmax=1.5, + DMmax=2000, + DMlines=nozlist, + ) + + main() From 2005096d4da7217b74716bd7b1a394a061405607 Mon Sep 17 00:00:00 2001 From: Jay-MBP Date: Wed, 29 Jun 2022 13:50:41 -0700 Subject: [PATCH 004/104] debugging z-dm relation grid spacing --- papers/H0_I/Figures/py/figs_zdm_H0_I.py | 12 +++++++++++- 1 file changed, 11 insertions(+), 1 deletion(-) diff --git a/papers/H0_I/Figures/py/figs_zdm_H0_I.py b/papers/H0_I/Figures/py/figs_zdm_H0_I.py index 7a33c3c1..6a65b065 100644 --- a/papers/H0_I/Figures/py/figs_zdm_H0_I.py +++ b/papers/H0_I/Figures/py/figs_zdm_H0_I.py @@ -602,6 +602,10 @@ def fig_craco_varyF_zDM( plt.sca(ax1) + ax = plt.gca() + dms, zeval = figm.average_DM(0.3, cumul=True) + ax.plot(zeval, dms) + plt.xlabel("z") plt.ylabel("${\\rm DM}_{\\rm EG}$") @@ -627,6 +631,10 @@ def fig_craco_varyF_zDM( # Update grid vparams = {} vparams["F"] = F + + vparams["lmean"] = 1e-3 + vparams["lsigma"] = 0.1 + if other_param == "Emax": vparams["lEmax"] = fiducial_Emax + scl elif other_param == "H0": @@ -654,6 +662,7 @@ def fig_craco_varyF_zDM( # sets the x and y tics # JXP fussing here!! + tvals, ticks = figures.ticks_pgrid(zvals, these_vals=zticks) # , fmt='str4') plt.xticks(tvals, ticks) tvals, ticks = figures.ticks_pgrid(dmvals, fmt="int") # , fmt='str4') @@ -676,6 +685,7 @@ def fig_craco_varyF_zDM( ###### gets decent axis labels, down to 1 decimal place ####### ax = plt.gca() + ax.legend(legend_lines, labels, loc="lower right") # Fontsize @@ -696,7 +706,7 @@ def fig_craco_varyF_zDM( # Finish plt.tight_layout() - plt.savefig(outfile, dpi=300) + plt.savefig(outfile, dpi=300, bbox_inches="tight") plt.close() print(f"Wrote: {outfile}") From 1f99cdf44f42f3d92784713b4a158a0a99343147 Mon Sep 17 00:00:00 2001 From: profxj Date: Wed, 29 Jun 2022 14:36:07 -0700 Subject: [PATCH 005/104] fussin about --- papers/F/Analysis/py/analy_F_I.py | 10 ++ papers/F/Figures/py/figs_zdm_F_I.py | 169 ++++++++++++++++++++++++ papers/H0_I/Figures/py/figs_zdm_H0_I.py | 145 -------------------- papers/H0_I/Figures/py/testing.py | 5 - 4 files changed, 179 insertions(+), 150 deletions(-) create mode 100644 papers/F/Analysis/py/analy_F_I.py create mode 100644 papers/F/Figures/py/figs_zdm_F_I.py delete mode 100644 papers/H0_I/Figures/py/testing.py diff --git a/papers/F/Analysis/py/analy_F_I.py b/papers/F/Analysis/py/analy_F_I.py new file mode 100644 index 00000000..3f8b2719 --- /dev/null +++ b/papers/F/Analysis/py/analy_F_I.py @@ -0,0 +1,10 @@ +from zdm.craco import loading + +fiducial_survey = 'CRACO_std_May2022' + +def craco_mc_survey_grid(): + """ Load the defaul MonteCarlo survey+grid for CRACO """ + survey, grid = loading.survey_and_grid( + survey_name=fiducial_survey, + NFRB=100, lum_func=2, iFRB=100) + return survey, grid diff --git a/papers/F/Figures/py/figs_zdm_F_I.py b/papers/F/Figures/py/figs_zdm_F_I.py new file mode 100644 index 00000000..7b77f242 --- /dev/null +++ b/papers/F/Figures/py/figs_zdm_F_I.py @@ -0,0 +1,169 @@ +import os, sys +import numpy as np + +from matplotlib import pyplot as plt +from scipy.interpolate import interp1d + +from frb.dm import igm as figm +from frb.figures import utils as fig_utils + +from zdm import figures + +sys.path.append(os.path.abspath("../Analysis/py")) +sys.path.append(os.path.abspath("../../Analysis/py")) +import analy_F_I + +def fig_craco_varyF_zDM( + outfile, + zmax=2.3, + DMmax=1500, + norm=2, + other_param="Emax", + Aconts=[0.05], + fuss_with_ticks: bool = False, +): + """_summary_ + + Args: + outfile (_type_): _description_ + zmax (float, optional): _description_. Defaults to 2.3. + DMmax (int, optional): _description_. Defaults to 1500. + norm (int, optional): _description_. Defaults to 2. + other_param (str, optional): _description_. Defaults to 'Emax'. + Aconts (list, optional): _description_. Defaults to [0.05]. + fuss_with_ticks (bool, optional): _description_. Defaults to False. + """ + # Generate the grid + survey, grid = analy_F_I.craco_mc_survey_grid() + + # survey, grid = loading.survey_and_grid( + # survey_name='CRACO_alpha1_Planck18_Gamma', + # NFRB=100, lum_func=2) + + fiducial_Emax = grid.state.energy.lEmax + fiducial_H0 = grid.state.cosmo.H0 + + plt.figure() + ax1 = plt.axes() + + plt.sca(ax1) + + + plt.xlabel("z") + plt.ylabel("${\\rm DM}_{\\rm EG}$") + + if other_param == "Emax": + F_values = [0.01, 0.3, 0.7, 0.9] + other_values = [0.0, 0.0, 0.0, -0.1] + lstyles = ["-", "-", "-", ":"] + zticks = [0.5, 1.0, 1.5, 2.0] + ylim = (0.0, DMmax) + elif other_param == "H0": + F_values = [0.01, 0.3, 0.7, 0.9] + other_values = [fiducial_H0, fiducial_H0, fiducial_H0, fiducial_H0] + lstyles = ["-", "-", "-", ":"] + zticks, ylim = None, None + + # Loop on grids + legend_lines = [] + labels = [] + for F, scl, lstyle, clr in zip( + F_values, other_values, lstyles, ["b", "k", "r", "gray"] + ): + + # Update grid + vparams = {} + vparams["F"] = F + + vparams["lmean"] = 1e-3 + vparams["lsigma"] = 0.1 + + if other_param == "Emax": + vparams["lEmax"] = fiducial_Emax + scl + elif other_param == "H0": + vparams["H0"] = scl + grid.update(vparams) + + # Unpack + full_zDMgrid, zvals, dmvals = ( + grid.rates.copy(), + grid.zvals.copy(), + grid.dmvals.copy(), + ) + + # currently this is "per cell" - now to change to "per DM" + # normalises the grid by the bin width, i.e. probability per bin, not probability density + + # checks against zeros for a log-plot + + zvals, dmvals, zDMgrid = figures.proc_pgrid( + full_zDMgrid, zvals, (0, zmax), dmvals, (0, DMmax) + ) + + + # Contours + alevels = figures.find_Alevels(full_zDMgrid, Aconts) + + # sets the x and y tics + # JXP fussing here!! + + tvals, ticks = figures.ticks_pgrid(zvals, these_vals=zticks) # , fmt='str4') + plt.xticks(tvals, ticks) + tvals, ticks = figures.ticks_pgrid(dmvals, fmt="int") # , fmt='str4') + plt.yticks(tvals, ticks) + + ax = plt.gca() + cs = ax.contour( + zDMgrid.T, levels=alevels, origin="lower", colors=[clr], linestyles=lstyle + ) + leg, _ = cs.legend_elements() + legend_lines.append(leg[0]) + + # Label + if other_param == "Emax": + labels.append( + r"$F = $" + f"{F}, log " + r"$E_{\rm max}$" + f"= {vparams['lEmax']}" + ) + elif other_param == "H0": + labels.append(r"$F = $" + f"{F}, H0 = {vparams['H0']}") + + ###### gets decent axis labels, down to 1 decimal place ####### + ax = plt.gca() + + # Interpolators + f_DM = interp1d(dmvals, np.arange(dmvals.size), fill_value='extrapolate', bounds_error=False) + f_z = interp1d(zvals, np.arange(zvals.size), fill_value='extrapolate', bounds_error=False) + + from astropy.cosmology import FlatLambdaCDM + cosmo = FlatLambdaCDM(H0=grid.state.cosmo.H0, + Ob0=grid.state.cosmo.Omega_b, + Om0=grid.state.cosmo.Omega_m) + dms, zeval = figm.average_DM(2.0, cumul=True, cosmo=cosmo) + + ax.plot(f_z(zeval), f_DM(dms), 'k--', label='Macquart Relation') + + ax.legend(legend_lines, labels, loc="lower right") + + # Fontsize + fig_utils.set_fontsize(ax, 16.0) + + # Ticks + if fuss_with_ticks: + labels = [item.get_text() for item in ax.get_xticklabels()] + for i in np.arange(len(labels)): + labels[i] = labels[i][0:4] + ax.set_xticklabels(labels) + labels = [item.get_text() for item in ax.get_yticklabels()] + for i in np.arange(len(labels)): + if "." in labels[i]: + labels[i] = labels[i].split(".")[0] + ax.set_yticklabels(labels) + ax.yaxis.labelpad = 0 + + # Finish + plt.tight_layout() + plt.savefig(outfile, dpi=300, bbox_inches="tight") + plt.close() + print(f"Wrote: {outfile}") + +fig_craco_varyF_zDM("test.pdf", other_param="H0") \ No newline at end of file diff --git a/papers/H0_I/Figures/py/figs_zdm_H0_I.py b/papers/H0_I/Figures/py/figs_zdm_H0_I.py index 6a65b065..a6b55f44 100644 --- a/papers/H0_I/Figures/py/figs_zdm_H0_I.py +++ b/papers/H0_I/Figures/py/figs_zdm_H0_I.py @@ -2,9 +2,7 @@ import os, sys from typing import IO import numpy as np -from numpy.lib.function_base import percentile import scipy -from scipy import stats import argparse @@ -567,149 +565,6 @@ def fig_fd_vs_z(outfile="fig_fd_vs_z.png"): print(f"Wrote: {outfile}") -def fig_craco_varyF_zDM( - outfile, - zmax=2.3, - DMmax=1500, - norm=2, - other_param="Emax", - Aconts=[0.05], - fuss_with_ticks: bool = False, -): - """_summary_ - - Args: - outfile (_type_): _description_ - zmax (float, optional): _description_. Defaults to 2.3. - DMmax (int, optional): _description_. Defaults to 1500. - norm (int, optional): _description_. Defaults to 2. - other_param (str, optional): _description_. Defaults to 'Emax'. - Aconts (list, optional): _description_. Defaults to [0.05]. - fuss_with_ticks (bool, optional): _description_. Defaults to False. - """ - # Generate the grid - survey, grid = analy_H0_I.craco_mc_survey_grid() - - # survey, grid = loading.survey_and_grid( - # survey_name='CRACO_alpha1_Planck18_Gamma', - # NFRB=100, lum_func=2) - - fiducial_Emax = grid.state.energy.lEmax - fiducial_H0 = grid.state.cosmo.H0 - - plt.figure() - ax1 = plt.axes() - - plt.sca(ax1) - - ax = plt.gca() - dms, zeval = figm.average_DM(0.3, cumul=True) - ax.plot(zeval, dms) - - plt.xlabel("z") - plt.ylabel("${\\rm DM}_{\\rm EG}$") - - if other_param == "Emax": - F_values = [0.01, 0.3, 0.7, 0.9] - other_values = [0.0, 0.0, 0.0, -0.1] - lstyles = ["-", "-", "-", ":"] - zticks = [0.5, 1.0, 1.5, 2.0] - ylim = (0.0, DMmax) - elif other_param == "H0": - F_values = [0.01, 0.3, 0.7, 0.9] - other_values = [fiducial_H0, fiducial_H0, fiducial_H0, fiducial_H0] - lstyles = ["-", "-", "-", ":"] - zticks, ylim = None, None - - # Loop on grids - legend_lines = [] - labels = [] - for F, scl, lstyle, clr in zip( - F_values, other_values, lstyles, ["b", "k", "r", "gray"] - ): - - # Update grid - vparams = {} - vparams["F"] = F - - vparams["lmean"] = 1e-3 - vparams["lsigma"] = 0.1 - - if other_param == "Emax": - vparams["lEmax"] = fiducial_Emax + scl - elif other_param == "H0": - vparams["H0"] = scl - grid.update(vparams) - - # Unpack - full_zDMgrid, zvals, dmvals = ( - grid.rates.copy(), - grid.zvals.copy(), - grid.dmvals.copy(), - ) - - # currently this is "per cell" - now to change to "per DM" - # normalises the grid by the bin width, i.e. probability per bin, not probability density - - # checks against zeros for a log-plot - - zvals, dmvals, zDMgrid = figures.proc_pgrid( - full_zDMgrid, zvals, (0, zmax), dmvals, (0, DMmax) - ) - - # Contours - alevels = figures.find_Alevels(full_zDMgrid, Aconts) - - # sets the x and y tics - # JXP fussing here!! - - tvals, ticks = figures.ticks_pgrid(zvals, these_vals=zticks) # , fmt='str4') - plt.xticks(tvals, ticks) - tvals, ticks = figures.ticks_pgrid(dmvals, fmt="int") # , fmt='str4') - plt.yticks(tvals, ticks) - - ax = plt.gca() - cs = ax.contour( - zDMgrid.T, levels=alevels, origin="lower", colors=[clr], linestyles=lstyle - ) - leg, _ = cs.legend_elements() - legend_lines.append(leg[0]) - - # Label - if other_param == "Emax": - labels.append( - r"$F = $" + f"{F}, log " + r"$E_{\rm max}$" + f"= {vparams['lEmax']}" - ) - elif other_param == "H0": - labels.append(r"$F = $" + f"{F}, H0 = {vparams['H0']}") - - ###### gets decent axis labels, down to 1 decimal place ####### - ax = plt.gca() - - ax.legend(legend_lines, labels, loc="lower right") - - # Fontsize - fig_utils.set_fontsize(ax, 16.0) - - # Ticks - if fuss_with_ticks: - labels = [item.get_text() for item in ax.get_xticklabels()] - for i in np.arange(len(labels)): - labels[i] = labels[i][0:4] - ax.set_xticklabels(labels) - labels = [item.get_text() for item in ax.get_yticklabels()] - for i in np.arange(len(labels)): - if "." in labels[i]: - labels[i] = labels[i].split(".")[0] - ax.set_yticklabels(labels) - ax.yaxis.labelpad = 0 - - # Finish - plt.tight_layout() - plt.savefig(outfile, dpi=300, bbox_inches="tight") - plt.close() - print(f"Wrote: {outfile}") - #### ########################## ######################### def main(pargs): diff --git a/papers/H0_I/Figures/py/testing.py b/papers/H0_I/Figures/py/testing.py deleted file mode 100644 index 42596782..00000000 --- a/papers/H0_I/Figures/py/testing.py +++ /dev/null @@ -1,5 +0,0 @@ -from figs_zdm_H0_I import fig_craco_varyF_zDM, fig_craco_varyH0_zDM - -fig_craco_varyF_zDM("test.pdf", other_param="H0") -# fig_craco_varyH0_zDM("test.pdf", other_param="F") - From 4bc79b6b0d0d60b974cadab2c5d2ee91404a4688 Mon Sep 17 00:00:00 2001 From: Jay-MBP Date: Mon, 4 Jul 2022 23:27:54 -0700 Subject: [PATCH 006/104] fix varyF --- papers/F/Analysis/py/analy_F_I.py | 7 +- papers/F/Figures/py/figs_zdm_F_I.py | 399 ++++++++++++++++++++++-- papers/H0_I/Figures/py/figs_zdm_H0_I.py | 1 - 3 files changed, 384 insertions(+), 23 deletions(-) diff --git a/papers/F/Analysis/py/analy_F_I.py b/papers/F/Analysis/py/analy_F_I.py index 3f8b2719..097c084c 100644 --- a/papers/F/Analysis/py/analy_F_I.py +++ b/papers/F/Analysis/py/analy_F_I.py @@ -1,10 +1,11 @@ from zdm.craco import loading -fiducial_survey = 'CRACO_std_May2022' +fiducial_survey = "CRACO_std_May2022" + def craco_mc_survey_grid(): """ Load the defaul MonteCarlo survey+grid for CRACO """ survey, grid = loading.survey_and_grid( - survey_name=fiducial_survey, - NFRB=100, lum_func=2, iFRB=100) + survey_name=fiducial_survey, NFRB=100, lum_func=2, iFRB=100 + ) return survey, grid diff --git a/papers/F/Figures/py/figs_zdm_F_I.py b/papers/F/Figures/py/figs_zdm_F_I.py index 7b77f242..310feb9a 100644 --- a/papers/F/Figures/py/figs_zdm_F_I.py +++ b/papers/F/Figures/py/figs_zdm_F_I.py @@ -7,12 +7,15 @@ from frb.dm import igm as figm from frb.figures import utils as fig_utils -from zdm import figures +from zdm import figures, pcosmic sys.path.append(os.path.abspath("../Analysis/py")) sys.path.append(os.path.abspath("../../Analysis/py")) import analy_F_I +from astropy.cosmology import FlatLambdaCDM + + def fig_craco_varyF_zDM( outfile, zmax=2.3, @@ -36,19 +39,16 @@ def fig_craco_varyF_zDM( # Generate the grid survey, grid = analy_F_I.craco_mc_survey_grid() - # survey, grid = loading.survey_and_grid( - # survey_name='CRACO_alpha1_Planck18_Gamma', - # NFRB=100, lum_func=2) - fiducial_Emax = grid.state.energy.lEmax fiducial_H0 = grid.state.cosmo.H0 + fiducial_lmean = grid.state.host.lmean + fiducial_lsigma = grid.state.host.lsigma plt.figure() ax1 = plt.axes() plt.sca(ax1) - plt.xlabel("z") plt.ylabel("${\\rm DM}_{\\rm EG}$") @@ -63,6 +63,11 @@ def fig_craco_varyF_zDM( other_values = [fiducial_H0, fiducial_H0, fiducial_H0, fiducial_H0] lstyles = ["-", "-", "-", ":"] zticks, ylim = None, None + elif other_param == "lmean": + F_values = [0.01, 0.32, 0.7, 0.32] + other_values = [0.0, 1.0, 0.0, 3.0] + lstyles = ["-", "-", "-", ":"] + zticks, ylim = None, None # Loop on grids legend_lines = [] @@ -75,6 +80,7 @@ def fig_craco_varyF_zDM( vparams = {} vparams["F"] = F + # Sets the log-normal distribution for DM_host to ~0. vparams["lmean"] = 1e-3 vparams["lsigma"] = 0.1 @@ -82,6 +88,10 @@ def fig_craco_varyF_zDM( vparams["lEmax"] = fiducial_Emax + scl elif other_param == "H0": vparams["H0"] = scl + elif other_param == "lmean": + # vparams["lsigma"] = fiducial_lsigma + vparams["lmean"] = scl + grid.update(vparams) # Unpack @@ -100,13 +110,10 @@ def fig_craco_varyF_zDM( full_zDMgrid, zvals, (0, zmax), dmvals, (0, DMmax) ) - # Contours alevels = figures.find_Alevels(full_zDMgrid, Aconts) - # sets the x and y tics - # JXP fussing here!! - + # Sets the x and y ticks tvals, ticks = figures.ticks_pgrid(zvals, these_vals=zticks) # , fmt='str4') plt.xticks(tvals, ticks) tvals, ticks = figures.ticks_pgrid(dmvals, fmt="int") # , fmt='str4') @@ -126,21 +133,31 @@ def fig_craco_varyF_zDM( ) elif other_param == "H0": labels.append(r"$F = $" + f"{F}, H0 = {vparams['H0']}") + elif other_param == "lmean": + labels.append(r"$F = $" + f"{F}, $\mu =$ {vparams['lmean']}") ###### gets decent axis labels, down to 1 decimal place ####### ax = plt.gca() # Interpolators - f_DM = interp1d(dmvals, np.arange(dmvals.size), fill_value='extrapolate', bounds_error=False) - f_z = interp1d(zvals, np.arange(zvals.size), fill_value='extrapolate', bounds_error=False) - - from astropy.cosmology import FlatLambdaCDM - cosmo = FlatLambdaCDM(H0=grid.state.cosmo.H0, - Ob0=grid.state.cosmo.Omega_b, - Om0=grid.state.cosmo.Omega_m) + f_DM = interp1d( + dmvals, np.arange(dmvals.size), fill_value="extrapolate", bounds_error=False + ) + f_z = interp1d( + zvals, np.arange(zvals.size), fill_value="extrapolate", bounds_error=False + ) + + cosmo = FlatLambdaCDM( + H0=grid.state.cosmo.H0, + Ob0=grid.state.cosmo.Omega_b, + Om0=grid.state.cosmo.Omega_m, + ) dms, zeval = figm.average_DM(2.0, cumul=True, cosmo=cosmo) - ax.plot(f_z(zeval), f_DM(dms), 'k--', label='Macquart Relation') + l_mqr = ax.plot(f_z(zeval), f_DM(dms), "k--") + + legend_lines.append(l_mqr[0]) + labels.append("Macquart Relation") ax.legend(legend_lines, labels, loc="lower right") @@ -166,4 +183,348 @@ def fig_craco_varyF_zDM( plt.close() print(f"Wrote: {outfile}") -fig_craco_varyF_zDM("test.pdf", other_param="H0") \ No newline at end of file + +### + + +def fig_varyF( + outfile, + zmax=2.3, + DMmax=1500, + other_param="lmean", + Aconts=[0.05], + F_values=[None], + other_values=[None], + lcolors=["b"], + lstyles=["-"], + zticks=None, + ylim=None, +): + + survey, grid = analy_F_I.craco_mc_survey_grid() + + fiducial_F = grid.state.IGM.F + fiducial_Emax = grid.state.energy.lEmax + fiducial_H0 = grid.state.cosmo.H0 + fiducial_lmean = grid.state.host.lmean + fiducial_lsigma = grid.state.host.lsigma + + fig, ax = plt.subplots(dpi=200) + + ax.set_xlabel("z") + ax.set_ylabel("${\\rm DM}_{\\rm EG}$") + + legend_lines = [] + labels = [] + + for F, other, lstyle, color in zip(F_values, other_values, lstyles, lcolors): + + vparams = {} + + if F is None: + F = fiducial_F + + vparams["F"] = F + + if other_param == "H0": + if other == None: + other = fiducial_H0 + vparams["H0"] = other + elif other_param == "Emax": + if other == None: + other = fiducial_Emax + vparams["Emax"] = other + other = fiducial_Emax + elif other_param == "lmean": + if other == None: + other = fiducial_lmean + vparams["lmean"] = other + other = fiducial_lmean + + grid.update(vparams) + + full_zDMgrid, zvals, dmvals = ( + grid.rates.copy(), + grid.zvals.copy(), + grid.dmvals.copy(), + ) + + zvals, dmvals, zDMgrid = figures.proc_pgrid( + full_zDMgrid, zvals, (0, zmax), dmvals, (0, DMmax) + ) + + alevels = figures.find_Alevels(full_zDMgrid, Aconts) + + plt.sca(ax) + + tvals, ticks = figures.ticks_pgrid(zvals, these_vals=zticks) + plt.xticks(tvals, ticks) + + tvals, ticks = figures.ticks_pgrid(dmvals, fmt="int") + plt.yticks(tvals, ticks) + + cs = ax.contour( + zDMgrid.T, levels=alevels, origin="lower", colors=[color], linestyles=lstyle + ) + + leg, _ = cs.legend_elements() + legend_lines.append(leg[0]) + + if other_param == "Emax": + labels.append( + r"$F = $" + f"{F}, log " + r"$E_{\rm max}$" + f"= {vparams['lEmax']}" + ) + elif other_param == "H0": + labels.append(r"$F = $" + f"{F}, H0 = {vparams['H0']}") + elif other_param == "lmean": + labels.append(r"$F = $" + f"{F}, $\mu =$ {vparams['lmean']}") + + # Interpolators + f_DM = interp1d( + dmvals, np.arange(dmvals.size), fill_value="extrapolate", bounds_error=False + ) + f_z = interp1d( + zvals, np.arange(zvals.size), fill_value="extrapolate", bounds_error=False + ) + + cosmo = FlatLambdaCDM( + H0=grid.state.cosmo.H0, + Ob0=grid.state.cosmo.Omega_b, + Om0=grid.state.cosmo.Omega_m, + ) + + dms, zeval = figm.average_DM(2.0, cumul=True, cosmo=cosmo) + + l_mqr = ax.plot(f_z(zeval), f_DM(dms), "k--") + + legend_lines.append(l_mqr[0]) + labels.append("Macquart Relation") + + ax.legend(legend_lines, labels, loc="lower right") + + # Fontsize + fig_utils.set_fontsize(ax, 16.0) + + fig.tight_layout() + plt.savefig(outfile, dpi=300, bbox_inches="tight") + plt.close() + print(f"Wrote: {outfile}") + + +### + + +def fig_craco_fiducial_F( + outfile="fig_craco_fiducial_F.png", + zmax=2.5, + DMmax=2500, + show_Macquart=False, + log=True, + label="$\\log_{10} \; p(DM_{\\rm EG},z)$", + Aconts=[0.01, 0.1, 0.5], + cmap="jet", + show=False, + figsize=None, + vmnx=(None, None), + grid=None, + survey=None, + F=0.03, + suppress_DM_host=False, +): + """ + Very complicated routine for plotting 2D zdm grids + Args: + zDMgrid ([type]): [description] + zvals ([type]): [description] + dmvals ([type]): [description] + zmax (int, optional): [description]. Defaults to 1. + DMmax (int, optional): [description]. Defaults to 1000. + norm (int, optional): [description]. Defaults to 0. + log (bool, optional): [description]. Defaults to True. + label (str, optional): [description]. Defaults to '$\log_{10}p(DM_{\rm EG},z)$'. + project (bool, optional): [description]. Defaults to False. + conts (bool, optional): [description]. Defaults to False. + FRBZ ([type], optional): [description]. Defaults to None. + FRBDM ([type], optional): [description]. Defaults to None. + Aconts (bool, optional): [description]. Defaults to False. + Macquart (state, optional): state object. Used to generat the Maquart relation. + Defaults to None. + title (str, optional): [description]. Defaults to "Plot". + H0 ([type], optional): [description]. Defaults to None. + showplot (bool, optional): [description]. Defaults to False. + """ + # Generate the grid + if grid is None or survey is None: + survey, grid = analy_F_I.craco_mc_survey_grid() + + fiducial_H0 = grid.state.cosmo.H0 + + vparams = {"H0": fiducial_H0, "F": F} + + if suppress_DM_host: + # Sets the log-normal distribution for DM_host to ~0. + vparams["lmean"] = 1e-3 + vparams["lsigma"] = 0.1 + + grid.update(vparams) + + # Unpack + full_zDMgrid, zvals, dmvals = grid.rates, grid.zvals, grid.dmvals + FRBZ = survey.frbs["Z"] + FRBDM = survey.DMEGs + + ##### imshow of grid ####### + fsize = 14.0 + plt.figure(figsize=figsize) + ax1 = plt.axes() + plt.sca(ax1) + + plt.xlabel("z") + plt.ylabel("${\\rm DM}_{\\rm EG}$") + # plt.title(title+str(H0)) + + # Cut down grid + zvals, dmvals, zDMgrid = figures.proc_pgrid( + full_zDMgrid, zvals, (0, zmax), dmvals, (0, DMmax) + ) + ddm = dmvals[1] - dmvals[0] + dz = zvals[1] - zvals[0] + nz, ndm = zDMgrid.shape + + # Contours + alevels = figures.find_Alevels(full_zDMgrid, Aconts, log=True) + + # Ticks + tvals, ticks = figures.ticks_pgrid(zvals) # , fmt='str4') + plt.xticks(tvals, ticks) + tvals, ticks = figures.ticks_pgrid(dmvals, fmt="int") # , fmt='str4') + plt.yticks(tvals, ticks) + + # Image + im = plt.imshow( + zDMgrid.T, + cmap=cmap, + origin="lower", + vmin=vmnx[0], + vmax=vmnx[1], + interpolation="None", + aspect="auto", + ) + + styles = ["--", "-.", ":"] + ax = plt.gca() + cs = ax.contour( + zDMgrid.T, levels=alevels, origin="lower", colors="white", linestyles=styles + ) + + ax = plt.gca() + + ax.set_title(f"F = {F}") + + muDMhost = np.log(10 ** grid.state.host.lmean) + sigmaDMhost = np.log(10 ** grid.state.host.lsigma) + meanHost = np.exp(muDMhost + sigmaDMhost ** 2 / 2.0) + medianHost = np.exp(muDMhost) + print(f"Host: mean={meanHost}, median={medianHost}") + plt.ylim(0, ndm - 1) + plt.xlim(0, nz - 1) + zmax = zvals[-1] + nz = zvals.size + # DMbar, zeval = igm.average_DM(zmax, cumul=True, neval=nz+1) + DM_cosmic = pcosmic.get_mean_DM(zvals, grid.state) + + # idea is that 1 point is 1, hence... + zeval = zvals / dz + DMEG_mean = (DM_cosmic + meanHost) / ddm + DMEG_median = (DM_cosmic + medianHost) / ddm + + # Check median + f_median = interp1d(zvals, DM_cosmic + medianHost, fill_value="extrapolate") + eval_DMEG = f_median(FRBZ) + above = FRBDM > eval_DMEG + print(f"There are {np.sum(above)/len(FRBZ)} above the median") + + if show_Macquart: + plt.plot( + zeval, + DMEG_mean, + color="gray", + linewidth=2, + label="Macquart relation (mean)", + ) + plt.plot( + zeval, + DMEG_median, + color="gray", + linewidth=2, + ls="--", + label="Macquart relation (median)", + ) + l = plt.legend(loc="lower right", fontsize=12) + # l=plt.legend(bbox_to_anchor=(0.2, 0.8),fontsize=8) + # for text in l.get_texts(): + # text.set_color("white") + + # limit to a reasonable range if logscale + if log and vmnx[0] is None: + themax = zDMgrid.max() + themin = int(themax - 4) + themax = int(themax) + plt.clim(themin, themax) + + ##### add FRB host galaxies at some DM/redshift ##### + if FRBZ is not None: + iDMs = FRBDM / ddm + iZ = FRBZ / dz + # Restrict to plot range + gd = (FRBDM < DMmax) & (FRBZ < zmax) + plt.plot(iZ[gd], iDMs[gd], "ko", linestyle="", markersize=2.0) + + cbar = plt.colorbar(im, fraction=0.046, shrink=1.2, aspect=15, pad=0.05) + cbar.set_label(label) + + fig_utils.set_fontsize(ax, fsize) + + plt.tight_layout() + + if show: + plt.show() + else: + plt.savefig(outfile, dpi=300) + print(f"Wrote: {outfile}") + plt.close() + + +### tests + +# fig_craco_varyF_zDM("contours_varyF_H0.pdf", other_param="H0") + +# fig_craco_fiducial_F("fig_craco_fiducial_F_0.32.png", show_Macquart=True, F=0.32, suppress_DM_host=True) +# fig_craco_fiducial_F("fig_craco_fiducial_F_0.01.png", show_Macquart=True, F=0.01, suppress_DM_host=True) +# fig_craco_fiducial_F("fig_craco_fiducial_F_0.9.png", show_Macquart=True, F=0.9, suppress_DM_host=True) + +# fig_craco_fiducial_F("fig_craco_fiducial_dmhost_F_0.32.png", show_Macquart=True, F=0.32, suppress_DM_host=False) +# fig_craco_fiducial_F("fig_craco_fiducial_dmhost_F_0.01.png", show_Macquart=True, F=0.01, suppress_DM_host=False) +# fig_craco_fiducial_F("fig_craco_fiducial_dmhost_F_0.9.png", show_Macquart=True, F=0.9, suppress_DM_host=False) + +# fig_craco_varyF_zDM("contours_varyF_lmean.pdf", other_param="lmean") + +fig_varyF( + "deg_basic.png", + other_param="lmean", + F_values=[0.01, 0.9], + other_values=[None, None], + lcolors=["r", "b"], + lstyles=["-", "-"], + DMmax=1800, +) + +fig_varyF( + "deg_other.png", + other_param="lmean", + F_values=[None, None], + other_values=[2.5, 1.5], + lcolors=["#e07a5f", "#81b29a"], + lstyles=["-", "-"], + DMmax=1800, +) diff --git a/papers/H0_I/Figures/py/figs_zdm_H0_I.py b/papers/H0_I/Figures/py/figs_zdm_H0_I.py index a6b55f44..6ee69fda 100644 --- a/papers/H0_I/Figures/py/figs_zdm_H0_I.py +++ b/papers/H0_I/Figures/py/figs_zdm_H0_I.py @@ -565,7 +565,6 @@ def fig_fd_vs_z(outfile="fig_fd_vs_z.png"): print(f"Wrote: {outfile}") - #### ########################## ######################### def main(pargs): From 6de6e6acb48fe45481a4e1d155b6853bc409398e Mon Sep 17 00:00:00 2001 From: profxj Date: Fri, 8 Jul 2022 04:38:26 -0700 Subject: [PATCH 007/104] prepping for mini --- .../F/Analysis/CRACO/Cloud/run_craco_mini.py | 96 +++++++++++++++++++ .../Analysis/CRACO/Cubes/craco_mini_cube.json | 64 +++++++++++++ 2 files changed, 160 insertions(+) create mode 100644 papers/F/Analysis/CRACO/Cloud/run_craco_mini.py create mode 100644 papers/F/Analysis/CRACO/Cubes/craco_mini_cube.json diff --git a/papers/F/Analysis/CRACO/Cloud/run_craco_mini.py b/papers/F/Analysis/CRACO/Cloud/run_craco_mini.py new file mode 100644 index 00000000..c0a5e9d8 --- /dev/null +++ b/papers/F/Analysis/CRACO/Cloud/run_craco_mini.py @@ -0,0 +1,96 @@ +""" Run a Nautilus test """ + +# It should be possible to remove all the matplotlib calls from this +# but in the current implementation it is not removed. +import argparse +import numpy as np +import os, sys + +from concurrent.futures import ProcessPoolExecutor +import subprocess + +from zdm import iteration as it +from zdm import io + +from IPython import embed + +def main(pargs, pfile:str, oproot:str, NFRB:int=None, iFRB:int=0, + outdir:str='Output'): + + # Generate the folder? + if not os.path.isdir(outdir): + os.mkdir(outdir) + + ############## Load up ############## + input_dict=io.process_jfile(pfile) + + # Deconstruct the input_dict + state_dict, cube_dict, vparam_dict = it.parse_input_dict(input_dict) + + npoints = np.array([item['n'] for key, item in vparam_dict.items()]) + ntotal = int(np.prod(np.abs(npoints))) + + # Total number of CPUs to be running on this Cube + total_ncpu = pargs.ncpu if pargs.total_ncpu is None else pargs.total_ncpu + batch = 1 if pargs.batch is None else pargs.batch + + nper_cpu = ntotal // total_ncpu + if int(ntotal/total_ncpu) != nper_cpu: + raise IOError(f"Ncpu={total_ncpu} must divide evenly into ntotal={ntotal}") + + commands = [] + for kk in range(pargs.ncpu): + line = [] + # Which CPU is running out of the total? + iCPU = (batch-1)*pargs.ncpu + kk + outfile = os.path.join(outdir, oproot.replace('.csv', f'{iCPU+1}.csv')) + # Command + line = ['zdm_build_cube', + '-n', f'{iCPU+1}', + '-m', f'{nper_cpu}', + '-o', f'{outfile}', + '-s', f'CRACO_alpha1_Planck18_Gamma', '--clobber', + '-p', f'{pfile}'] + # NFRB? + if NFRB is not None: + line += [f'--NFRB', f'{NFRB}'] + # iFRB? + if iFRB > 0: + line += [f'--iFRB', f'{iFRB}'] + # Finish + #line += ' & \n' + commands.append(line) + + # Launch em! + processes = [] + for command in commands: + # Popen + print(f"Running this command: {' '.join(command)}") + pw = subprocess.Popen(command) + processes.append(pw) + + # Wait on em! + for pw in processes: + pw.wait() + + print("All done!") + +def parse_option(): + # test for command-line arguments here + parser = argparse.ArgumentParser() + parser.add_argument('-n','--ncpu',type=int, required=True,help="Number of CPUs to run on (might be split in batches)") + parser.add_argument('-t','--total_ncpu',type=int, required=False,help="Total number of CPUs to run on (might be split in batches)") + parser.add_argument('-b','--batch',type=int, default=1, required=False,help="Batch number") + #parser.add_argument('--NFRB',type=int,required=False,help="Number of FRBs to analzye") + #parser.add_argument('--iFRB',type=int,default=0,help="Initial FRB to run from") + args = parser.parse_args() + + return args + + +if __name__ == "__main__": + # get the argument of training. + pfile = '../Cubes/craco_mini_cube.json' + oproot = 'craco_mini.csv' + pargs = parse_option() + main(pargs, pfile, oproot, NFRB=100, iFRB=100) diff --git a/papers/F/Analysis/CRACO/Cubes/craco_mini_cube.json b/papers/F/Analysis/CRACO/Cubes/craco_mini_cube.json new file mode 100644 index 00000000..a4271c13 --- /dev/null +++ b/papers/F/Analysis/CRACO/Cubes/craco_mini_cube.json @@ -0,0 +1,64 @@ +{ + "state": { + "energy": { + "luminosity_function": 2 + }, + "FRBdemo": { + "alpha_method": 1 + }, + "cosmo": { + "fix_Omega_b_h2": true + } + }, + "cube": { + "parameter_order": ["lC", "sfr_n", "alpha", "lEmax", "lmean", "lsigma", "gamma", "H0"] + }, + "lEmax": { + "DC": "energy", + "min": 40.5, + "max": 42.5, + "n": 50 + }, + "H0": { + "DC": "cosmo", + "min": 55.0, + "max": 80.0, + "n": 25 + }, + "alpha": { + "DC": "energy", + "min": 0.2, + "max": 2.0, + "n": 3 + }, + "gamma": { + "DC": "energy", + "min": -0.5, + "max": -1.5, + "n": 10 + }, + "sfr_n": { + "DC": "FRBdemo", + "min": 0.0, + "max": 3.0, + "n": 20 + }, + "lmean": { + "DC": "host", + "min": 1.7, + "max": 2.5, + "n": 5 + }, + "lsigma": { + "DC": "host", + "min": 0.3, + "max": 0.7, + "n": 5 + }, + "lC": { + "DC": "FRBdemo", + "min": -0.911, + "max": -0.911, + "n": -1 + } +} \ No newline at end of file From a3fb0330076073a16e3a311ba83ac7fc1908bcd3 Mon Sep 17 00:00:00 2001 From: Jay-MBP Date: Fri, 8 Jul 2022 12:39:38 -0700 Subject: [PATCH 008/104] begin MC tests on varying F --- papers/F/Figures/py/figs_zdm_F_I.py | 52 ++-- zdm/craco/mc.py | 465 ++++++++++++++++++---------- zdm/craco/testing.py | 150 +++++---- zdm/misc_functions.py | 13 +- 4 files changed, 435 insertions(+), 245 deletions(-) diff --git a/papers/F/Figures/py/figs_zdm_F_I.py b/papers/F/Figures/py/figs_zdm_F_I.py index 310feb9a..771175b2 100644 --- a/papers/F/Figures/py/figs_zdm_F_I.py +++ b/papers/F/Figures/py/figs_zdm_F_I.py @@ -89,7 +89,7 @@ def fig_craco_varyF_zDM( elif other_param == "H0": vparams["H0"] = scl elif other_param == "lmean": - # vparams["lsigma"] = fiducial_lsigma + vparams["lsigma"] = fiducial_lsigma vparams["lmean"] = scl grid.update(vparams) @@ -509,22 +509,34 @@ def fig_craco_fiducial_F( # fig_craco_varyF_zDM("contours_varyF_lmean.pdf", other_param="lmean") -fig_varyF( - "deg_basic.png", - other_param="lmean", - F_values=[0.01, 0.9], - other_values=[None, None], - lcolors=["r", "b"], - lstyles=["-", "-"], - DMmax=1800, -) - -fig_varyF( - "deg_other.png", - other_param="lmean", - F_values=[None, None], - other_values=[2.5, 1.5], - lcolors=["#e07a5f", "#81b29a"], - lstyles=["-", "-"], - DMmax=1800, -) +# fig_varyF( +# "deg_basic.png", +# other_param="lmean", +# F_values=[0.01, 0.9], +# other_values=[None, None], +# lcolors=["r", "b"], +# lstyles=["-", "-"], +# DMmax=1800, +# ) + +# fig_varyF( +# "deg_other.png", +# other_param="lmean", +# F_values=[None, None], +# other_values=[2.5, 1.5], +# lcolors=["#e07a5f", "#81b29a"], +# lstyles=["-", "-"], +# DMmax=1800, +# ) + +# fig_varyF( +# "test.png", +# other_param="lmean", +# F_values=[None, None], +# other_values=[1.0, 3.0], +# lcolors=["#e07a5f", "#81b29a"], +# lstyles=["-", "-"], +# DMmax=1800, +# ) + +fig_craco_varyF_zDM("strawberry.png", other_param="lmean") diff --git a/zdm/craco/mc.py b/zdm/craco/mc.py index 4682c846..6f70060e 100644 --- a/zdm/craco/mc.py +++ b/zdm/craco/mc.py @@ -1,4 +1,3 @@ - """ This script generates MC samples for CRAFT CRACO. @@ -22,273 +21,403 @@ import matplotlib.pyplot as plt import matplotlib -matplotlib.rcParams['image.interpolation'] = None +matplotlib.rcParams["image.interpolation"] = None + +defaultsize = 14 +ds = 4 +font = {"family": "normal", "weight": "normal", "size": defaultsize} +matplotlib.rc("font", **font) -defaultsize=14 -ds=4 -font = {'family' : 'normal', - 'weight' : 'normal', - 'size' : defaultsize} -matplotlib.rc('font', **font) -def generate(alpha_method=1, Nsamples=10000, do_plots=True, - lum_func=0, base_survey='CRAFT_CRACO_MC_base', - outfile='FRBs.txt', - savefile=None): +def generate( + alpha_method=1, + Nsamples=10000, + do_plots=True, + lum_func=0, + base_survey="CRAFT_CRACO_MC_base", + outfile="FRBs.txt", + savefile=None, + update_params=None, + plotfile="MC_Plots/mc_frbs_best_zdm_grid.pdf", +): craco, grid = loading.survey_and_grid( - alpha_method=alpha_method, - survey_name=base_survey, - lum_func=lum_func) - - print("Generating ",Nsamples," samples from CRACO survey/grid ") - sample=grid.GenMCSample(Nsamples) - sample=np.array(sample) + alpha_method=alpha_method, survey_name=base_survey, lum_func=lum_func + ) + + if update_params is not None: + grid.update(update_params) + + print("Generating ", Nsamples, " samples from CRACO survey/grid ") + sample = grid.GenMCSample(Nsamples) + sample = np.array(sample) if savefile is not None: - np.save(savefile,sample) - + np.save(savefile, sample) + if do_plots: - Zs = sample[:,0] - DMEGs = sample[:,1] + Zs = sample[:, 0] + DMEGs = sample[:, 1] misc_functions.plot_grid_2( - grid.rates,grid.zvals,grid.dmvals, - #FRBZ=craco.frbs["Z"],FRBDM=craco.DMEGs, - FRBZ=Zs, FRBDM=DMEGs, - zmax=1.8,DMmax=2000, - name='MC_Plots/mc_frbs_best_zdm_grid.pdf',norm=2, - log=True,label='$\\log_{10} p({\\rm DM}_{\\rm EG},z)$', - project=False,Aconts=[0.01,0.1,0.5],Macquart=grid.state) - - #sample + grid.rates, + grid.zvals, + grid.dmvals, + # FRBZ=craco.frbs["Z"],FRBDM=craco.DMEGs, + FRBZ=Zs, + FRBDM=DMEGs, + zmax=1.8, + DMmax=2000, + name=plotfile, + norm=2, + log=True, + label="$\\log_{10} p({\\rm DM}_{\\rm EG},z)$", + project=False, + Aconts=[0.01, 0.1, 0.5], + Macquart=grid.state, + ) + + # sample # 0: z # 1: DM # 2: b # 3: w # 4: s # plot some sample plots - do_basic_sample_plots(sample,opdir='MC_Plots') + do_basic_sample_plots(sample, opdir="MC_Plots") # Read base - sdir = os.path.join(resource_filename('zdm', 'craco'), - 'MC_Surveys') - basefile = os.path.join(sdir, base_survey+'.dat') - with open(basefile, 'r') as f: + sdir = os.path.join(resource_filename("zdm", "craco"), "MC_Surveys") + basefile = os.path.join(sdir, base_survey + ".dat") + with open(basefile, "r") as f: base_lines = f.readlines() # Write FRBs to disk add_header = True - with open(outfile, 'w') as f: + with open(outfile, "w") as f: # Header for base_line in base_lines: if add_header: - if 'NFRB' in base_line: - f.write(f'NFRB {Nsamples}\n') - elif 'NORM_FRB' in base_line: - f.write(f'NORM_FRB {Nsamples}\n') + if "NFRB" in base_line: + f.write(f"NFRB {Nsamples}\n") + elif "NORM_FRB" in base_line: + f.write(f"NORM_FRB {Nsamples}\n") else: f.write(base_line) - if 'fake data' in base_line: + if "fake data" in base_line: add_header = False - # + # for i in np.arange(Nsamples): - DMG=35 - DMEG=sample[i,1] - DMtot=DMEG+DMG+grid.state.MW.DMhalo - SNRTHRESH=9.5 - SNR=SNRTHRESH*sample[i,4] - z=sample[i,0] - w=sample[i,3] - - string="FRB "+str(i)+' {:6.1f} 35 {:6.1f} {:5.3f} {:5.1f} {:5.1f} \n'.format(DMtot,DMEG,z,SNR,w) - #print("FRB ",i,DMtot,SNR,DMEG,w) + DMG = 35 + DMEG = sample[i, 1] + DMtot = DMEG + DMG + grid.state.MW.DMhalo + SNRTHRESH = 9.5 + SNR = SNRTHRESH * sample[i, 4] + z = sample[i, 0] + w = sample[i, 3] + + string = ( + "FRB " + + str(i) + + " {:6.1f} 35 {:6.1f} {:5.3f} {:5.1f} {:5.1f} \n".format( + DMtot, DMEG, z, SNR, w + ) + ) + # print("FRB ",i,DMtot,SNR,DMEG,w) f.write(string) print(f"Wrote: {outfile}") # Write state - state_file = outfile.replace('.dat', '_state.json') + state_file = outfile.replace(".dat", "_state.json") grid.state.write(state_file) print(f"Wrote: {state_file}") - #evaluate_mc_sample_v1(g,s,pset,sample) - #evaluate_mc_sample_v2(g,s,pset,sample) - - -def evaluate_mc_sample_v1(grid,survey,pset,sample,opdir='Plots'): + # evaluate_mc_sample_v1(g,s,pset,sample) + # evaluate_mc_sample_v2(g,s,pset,sample) + + +def evaluate_mc_sample_v1(grid, survey, pset, sample, opdir="Plots"): """ Evaluates the likelihoods for an MC sample of events Simply replaces individual sets of z, DM, s with MC sets Will produce a plot of Nsamples/NFRB pseudo datasets. """ - t0=time.process_time() - - nsamples=sample.shape[0] - + t0 = time.process_time() + + nsamples = sample.shape[0] + # get number of FRBs per sample - Npersurvey=survey.NFRB + Npersurvey = survey.NFRB # determines how many false surveys we have stats for - Nsurveys=int(nsamples/Npersurvey) - - print("We can evaluate ",Nsurveys,"MC surveys given a total of ",nsamples," and ",Npersurvey," FRBs in the original data") - + Nsurveys = int(nsamples / Npersurvey) + + print( + "We can evaluate ", + Nsurveys, + "MC surveys given a total of ", + nsamples, + " and ", + Npersurvey, + " FRBs in the original data", + ) + # makes a deep copy of the survey - s=copy.deepcopy(survey) - - lls=[] - #Data order is DM,z,b,w,s + s = copy.deepcopy(survey) + + lls = [] + # Data order is DM,z,b,w,s # we loop through, artificially altering the survey with the composite values. for i in np.arange(Nsurveys): - this_sample=sample[i*Npersurvey:(i+1)*Npersurvey,:] - s.DMEGs=this_sample[:,0] - s.Ss=this_sample[:,4] - if s.nD==1: # DM, snr only - ll=it.calc_likelihoods_1D(grid,s,pset,psnr=True,Pn=True,dolist=0) + this_sample = sample[i * Npersurvey : (i + 1) * Npersurvey, :] + s.DMEGs = this_sample[:, 0] + s.Ss = this_sample[:, 4] + if s.nD == 1: # DM, snr only + ll = it.calc_likelihoods_1D(grid, s, pset, psnr=True, Pn=True, dolist=0) else: - s.Zs=this_sample[:,1] - ll=it.calc_likelihoods_2D(grid,s,pset,psnr=True,Pn=True,dolist=0) + s.Zs = this_sample[:, 1] + ll = it.calc_likelihoods_2D(grid, s, pset, psnr=True, Pn=True, dolist=0) lls.append(ll) - t1=time.process_time() - dt=t1-t0 - print("Finished after ",dt," seconds") - - lls=np.array(lls) - + t1 = time.process_time() + dt = t1 - t0 + print("Finished after ", dt, " seconds") + + lls = np.array(lls) + plt.figure() - plt.hist(lls,bins=20) - plt.xlabel('log likelihoods [log10]') - plt.ylabel('p(ll)') + plt.hist(lls, bins=20) + plt.xlabel("log likelihoods [log10]") + plt.ylabel("p(ll)") plt.xticks(rotation=90) plt.tight_layout() - plt.savefig(opdir+'/ll_histogram.pdf') + plt.savefig(opdir + "/ll_histogram.pdf") plt.close() -def evaluate_mc_sample_v2(grid,survey,pset,sample,opdir='Plots',Nsubsamp=1000): +def evaluate_mc_sample_v2(grid, survey, pset, sample, opdir="Plots", Nsubsamp=1000): """ Evaluates the likelihoods for an MC sample of events First, gets likelihoods for entire set of FRBs Then re-samples as needed, a total of Nsubsamp times """ - t0=time.process_time() - - nsamples=sample.shape[0] - + t0 = time.process_time() + + nsamples = sample.shape[0] + # makes a deep copy of the survey - s=copy.deepcopy(survey) - NFRBs=s.NFRB - - s.NFRB=nsamples # NOTE: does NOT change the assumed normalised FRB total! - s.DMEGs=sample[:,1] - s.Ss=sample[:,4] - if s.nD==1: # DM, snr only - llsum,lllist,expected,longlist=it.calc_likelihoods_1D(grid,s,pset,psnr=True,Pn=True,dolist=2) + s = copy.deepcopy(survey) + NFRBs = s.NFRB + + s.NFRB = nsamples # NOTE: does NOT change the assumed normalised FRB total! + s.DMEGs = sample[:, 1] + s.Ss = sample[:, 4] + if s.nD == 1: # DM, snr only + llsum, lllist, expected, longlist = it.calc_likelihoods_1D( + grid, s, pset, psnr=True, Pn=True, dolist=2 + ) else: - s.Zs=sample[:,0] - llsum,lllist,expected,longlist=it.calc_likelihoods_2D(grid,s,pset,psnr=True,Pn=True,dolist=2) - + s.Zs = sample[:, 0] + llsum, lllist, expected, longlist = it.calc_likelihoods_2D( + grid, s, pset, psnr=True, Pn=True, dolist=2 + ) + # we should preserve the normalisation factor for Tobs from lllist - Pzdm,Pn,Psnr=lllist - + Pzdm, Pn, Psnr = lllist + # plots histogram of individual FRB likelihoods including Psnr and Pzdm plt.figure() - plt.hist(longlist,bins=100) - plt.xlabel('Individual Psnr,Pzdm log likelihoods [log10]') - plt.ylabel('p(ll)') + plt.hist(longlist, bins=100) + plt.xlabel("Individual Psnr,Pzdm log likelihoods [log10]") + plt.ylabel("p(ll)") plt.tight_layout() - plt.savefig(opdir+'/individual_ll_histogram.pdf') + plt.savefig(opdir + "/individual_ll_histogram.pdf") plt.close() - + # generates many sub-samples of the data - lltots=[] + lltots = [] for i in np.arange(Nsubsamp): - thislist=np.random.choice(longlist,NFRBs) # samples with replacement, by default - lltot=Pn+np.sum(thislist) + thislist = np.random.choice( + longlist, NFRBs + ) # samples with replacement, by default + lltot = Pn + np.sum(thislist) lltots.append(lltot) - + plt.figure() - plt.hist(lltots,bins=20) - plt.xlabel('log likelihoods [log10]') - plt.ylabel('p(ll)') + plt.hist(lltots, bins=20) + plt.xlabel("log likelihoods [log10]") + plt.ylabel("p(ll)") plt.xticks(rotation=90) plt.tight_layout() - plt.savefig(opdir+'/sampled_ll_histogram.pdf') + plt.savefig(opdir + "/sampled_ll_histogram.pdf") plt.close() - - t1=time.process_time() - dt=t1-t0 - print("Finished after ",dt," seconds") - - -def do_basic_sample_plots(sample,opdir='Plots'): + + t1 = time.process_time() + dt = t1 - t0 + print("Finished after ", dt, " seconds") + + +def do_basic_sample_plots(sample, opdir="Plots"): """ Data order is DM,z,b,w,s """ if not os.path.exists(opdir): os.mkdir(opdir) - zs=sample[:,0] - DMs=sample[:,1] + zs = sample[:, 0] + DMs = sample[:, 1] plt.figure() - plt.hist(DMs,bins=100) - plt.xlabel('DM') - plt.ylabel('Sampled DMs') + plt.hist(DMs, bins=100) + plt.xlabel("DM") + plt.ylabel("Sampled DMs") plt.tight_layout() - plt.savefig(opdir+'/DM_histogram.pdf') + plt.savefig(opdir + "/DM_histogram.pdf") plt.close() - + plt.figure() - plt.hist(zs,bins=100) - plt.xlabel('z') - plt.ylabel('Sampled redshifts') + plt.hist(zs, bins=100) + plt.xlabel("z") + plt.ylabel("Sampled redshifts") plt.tight_layout() - plt.savefig(opdir+'/z_histogram.pdf') + plt.savefig(opdir + "/z_histogram.pdf") plt.close() - - bs=sample[:,2] + + bs = sample[:, 2] plt.figure() - plt.hist(np.log10(bs),bins=5) - plt.xlabel('log10 beam value') - plt.yscale('log') - plt.ylabel('Sampled beam bin') + plt.hist(np.log10(bs), bins=5) + plt.xlabel("log10 beam value") + plt.yscale("log") + plt.ylabel("Sampled beam bin") plt.tight_layout() - plt.savefig(opdir+'/b_histogram.pdf') + plt.savefig(opdir + "/b_histogram.pdf") plt.close() - - ws=sample[:,3] + + ws = sample[:, 3] plt.figure() - plt.hist(ws,bins=5) - plt.xlabel('width bin (not actual width!)') - plt.ylabel('Sampled width bin') - plt.yscale('log') + plt.hist(ws, bins=5) + plt.xlabel("width bin (not actual width!)") + plt.ylabel("Sampled width bin") + plt.yscale("log") plt.tight_layout() - plt.savefig(opdir+'/w_histogram.pdf') + plt.savefig(opdir + "/w_histogram.pdf") plt.close() - - s=sample[:,4] + + s = sample[:, 4] plt.figure() - plt.hist(np.log10(s),bins=100) - plt.xlabel('$\\log_{10} (s={\\rm SNR}/{\\rm SNR}_{\\rm th})$') - plt.yscale('log') - plt.ylabel('Sampled $s$') + plt.hist(np.log10(s), bins=100) + plt.xlabel("$\\log_{10} (s={\\rm SNR}/{\\rm SNR}_{\\rm th})$") + plt.yscale("log") + plt.ylabel("Sampled $s$") plt.tight_layout() - plt.savefig(opdir+'/s_histogram.pdf') + plt.savefig(opdir + "/s_histogram.pdf") plt.close() - + # Generate em! # Default run with Planck18 -#generate(alpha_method=1, Nsamples=5000, do_plots=True, +# generate(alpha_method=1, Nsamples=5000, do_plots=True, # outfile='MC_Surveys/CRACO_alpha1_Planck18.dat', # savefile=None) -''' Run in February 2022 +""" Run in February 2022 # Gamma function for energies generate(alpha_method=1, lum_func=2, Nsamples=5000, do_plots=True, outfile='MC_Surveys/CRACO_alpha1_Planck18_Gamma.dat', savefile=None) -''' +""" # Made in May 2022 -generate(alpha_method=1, lum_func=2, - Nsamples=5000, do_plots=True, - outfile='MC_Surveys/CRACO_std_May2022.dat', - savefile=None) \ No newline at end of file +# generate( +# alpha_method=1, +# lum_func=2, +# Nsamples=5000, +# do_plots=True, +# outfile="MC_Surveys/CRACO_std_May2022.dat", +# savefile=None, +# ) + +# JB + +generate( + alpha_method=1, + lum_func=2, + Nsamples=1000, + do_plots=True, + outfile="MC_F/Surveys/F_vanilla_survey.dat", + plotfile="MC_F/Plots/F_vanilla.pdf", + savefile=None, + # update_params={"F": 0.01}, +) + +# generate( +# alpha_method=1, +# lum_func=2, +# Nsamples=1000, +# do_plots=True, +# outfile="MC_F/Surveys/F_0.01_survey.dat", +# plotfile="MC_F/Plots/F_0.01.pdf", +# savefile=None, +# update_params={"F": 0.01}, +# ) + +# generate( +# alpha_method=1, +# lum_func=2, +# Nsamples=1000, +# do_plots=True, +# outfile="MC_F/Surveys/F_0.9_survey.dat", +# plotfile="MC_F/Plots/F_0.9.pdf", +# savefile=None, +# update_params={"F": 0.9}, +# ) + +# generate( +# alpha_method=1, +# lum_func=2, +# Nsamples=1000, +# do_plots=True, +# outfile="MC_F/Surveys/F_0.7_survey.dat", +# plotfile="MC_F/Plots/F_0.7.pdf", +# savefile=None, +# update_params={"F": 0.7}, +# ) + +generate( + alpha_method=1, + lum_func=2, + Nsamples=1000, + do_plots=True, + outfile="MC_F/Surveys/F_0.01_dmhost_suppressed_survey.dat", + plotfile="MC_F/Plots/F_0.01_dmhost_suppressed.pdf", + savefile=None, + update_params={"F": 0.01, "lmean": 1e-3, "lsigma": 0.1}, +) + +generate( + alpha_method=1, + lum_func=2, + Nsamples=1000, + do_plots=True, + outfile="MC_F/Surveys/F_0.9_dmhost_suppressed_survey.dat", + plotfile="MC_F/Plots/F_0.9_dmhost_suppressed.pdf", + savefile=None, + update_params={"F": 0.9, "lmean": 1e-3, "lsigma": 0.1}, +) + +generate( + alpha_method=1, + lum_func=2, + Nsamples=1000, + do_plots=True, + outfile="MC_F/Surveys/F_0.7_dmhost_suppressed_survey.dat", + plotfile="MC_F/Plots/F_0.7_dmhost_suppressed.pdf", + savefile=None, + update_params={"F": 0.7, "lmean": 1e-3, "lsigma": 0.1}, +) + +generate( + alpha_method=1, + lum_func=2, + Nsamples=1000, + do_plots=True, + outfile="MC_F/Surveys/F_vanilla_dmhost_suppressed_survey.dat", + plotfile="MC_F/Plots/F_vanilla_dmhost_suppressed.pdf", + savefile=None, + update_params={"lmean": 1e-3, "lsigma": 0.1}, +) diff --git a/zdm/craco/testing.py b/zdm/craco/testing.py index 0a183b5a..31b8683d 100644 --- a/zdm/craco/testing.py +++ b/zdm/craco/testing.py @@ -1,7 +1,7 @@ """ Run tests with CRACO FRBs """ ###### -# first run this to generate surveys and parameter sets, by +# first run this to generate surveys and parameter sets, by # setting NewSurveys=True NewGrids=True # Then set these to False and run with command line arguments # to generate *many* outputs @@ -19,64 +19,71 @@ from IPython import embed -matplotlib.rcParams['image.interpolation'] = None +matplotlib.rcParams["image.interpolation"] = None -defaultsize=14 -ds=4 -font = {'family' : 'normal', - 'weight' : 'normal', - 'size' : defaultsize} -matplotlib.rc('font', **font) +defaultsize = 14 +ds = 4 +font = {"family": "normal", "weight": "normal", "size": defaultsize} +matplotlib.rc("font", **font) + +# import igm +defaultsize = 14 +ds = 4 +font = {"family": "normal", "weight": "normal", "size": defaultsize} +matplotlib.rc("font", **font) -#import igm -defaultsize=14 -ds=4 -font = {'family' : 'normal', - 'weight' : 'normal', - 'size' : defaultsize} -matplotlib.rc('font', **font) def main(pargs): - isurvey, igrid = loading.survey_and_grid(survey_name=pargs.survey, - NFRB=pargs.nFRB, - iFRB=pargs.iFRB, - lum_func=pargs.lum_func) - surveys = [isurvey] + isurvey, igrid = loading.survey_and_grid( + survey_name=pargs.survey, + NFRB=pargs.nFRB, + iFRB=pargs.iFRB, + lum_func=pargs.lum_func, + ) + + # suppress DM host + igrid.update( + { + "lmean": 1e-3, + "lsigma": 0.1 + } + ) + + surveys = [isurvey] grids = [igrid] pvals = np.linspace(pargs.min, pargs.max, pargs.nstep) vparams = {} vparams[pargs.param] = None - vparams['lC'] = -0.9 + vparams["lC"] = -0.9 # DEBUGGING - #print("WARNING: REMOVE THE LINE BELOW WHEN DONE DEBUGGING") - #vparams['lEmax'] = 40.6 + # print("WARNING: REMOVE THE LINE BELOW WHEN DONE DEBUGGING") + # vparams['lEmax'] = 40.6 lls = [] nterms = [] # LL term related to norm (i.e. rates) pvterms = [] # LL term related to norm (i.e. rates) - pvvals = [] # - wzvals = [] # + pvvals = [] # + wzvals = [] # for tt, pval in enumerate(pvals): vparams[pargs.param] = pval - C,llC=it.minimise_const_only( - vparams,grids,surveys, Verbose=False) + C, llC = it.minimise_const_only(vparams, grids, surveys, Verbose=False) # Set lC - vparams['lC']=C + vparams["lC"] = C igrid.state.FRBdemo.lC = C # Grab final LL lls_final, nterm, pvterm, lpvals, lwz = it.calc_likelihoods_2D( - igrid, isurvey, - norm=True,psnr=True,dolist=4) + igrid, isurvey, norm=True, psnr=True, dolist=4 + ) # Hold lls.append(lls_final) nterms.append(nterm) pvterms.append(pvterm) pvvals.append(lpvals) wzvals.append(lwz) - print(f'{pargs.param}: pval={pval}, C={C}, lltot={lls_final}') + print(f"{pargs.param}: pval={pval}, C={C}, lltot={lls_final}") # Max imx = np.nanargmax(lls) @@ -85,16 +92,16 @@ def main(pargs): # Plot plt.clf() ax = plt.gca() - ax.plot(pvals, lls, 'o') + ax.plot(pvals, lls, "o") # Nan bad = np.isnan(lls) nbad = np.sum(bad) if nbad > 0: - ax.plot(pvals[bad], [np.nanmin(lls)]*nbad, 'x', color='r') + ax.plot(pvals[bad], [np.nanmin(lls)] * nbad, "x", color="r") ax.set_xlabel(pargs.param) - ax.set_ylabel('LL') + ax.set_ylabel("LL") # Max - ax.axvline(pvals[imx], color='g', ls='--', label=f'max={pvals[imx]}') + ax.axvline(pvals[imx], color="g", ls="--", label=f"max={pvals[imx]}") ax.legend() # Save? if pargs.opfile is not None: @@ -107,39 +114,63 @@ def main(pargs): # Plot nterm plt.clf() ax = plt.gca() - ax.plot(pvals, nterms, 'o') + ax.plot(pvals, nterms, "o") ax.set_xlabel(pargs.param) - ax.set_ylabel('nterm') - plt.savefig('nterms.png') + ax.set_ylabel("nterm") + plt.savefig("nterms.png") plt.close() # Plot nterm plt.clf() ax = plt.gca() - ax.plot(pvals, pvterms, 'o') + ax.plot(pvals, pvterms, "o") ax.set_xlabel(pargs.param) - ax.set_ylabel('pvterm') - plt.savefig('pvterms.png') + ax.set_ylabel("pvterm") + plt.savefig("pvterms.png") plt.close() + # command-line arguments here parser = argparse.ArgumentParser() -parser.add_argument('param',type=str,help="paramter to test on") -parser.add_argument('min',type=float,help="minimum value") -parser.add_argument('max',type=float,help="maximum value") -parser.add_argument('--nstep',type=int,default=10,required=False,help="number of steps") -parser.add_argument('--nFRB',type=int,default=1000,required=False,help="number of FRBs to analyze") -parser.add_argument('--iFRB',type=int,default=0,required=False,help="starting number of FRBs to analyze") -parser.add_argument('-o','--opfile',type=str,required=False,help="Output file for the data") -parser.add_argument('--survey',type=str,default='CRACO_std_May2022', - required=False,help="Survey name") -parser.add_argument('--lum_func',type=int,default=0, required=False,help="Luminosity function (0=power-law, 1=gamma)") +parser.add_argument("param", type=str, help="paramter to test on") +parser.add_argument("min", type=float, help="minimum value") +parser.add_argument("max", type=float, help="maximum value") +parser.add_argument( + "--nstep", type=int, default=10, required=False, help="number of steps" +) +parser.add_argument( + "--nFRB", type=int, default=1000, required=False, help="number of FRBs to analyze" +) +parser.add_argument( + "--iFRB", + type=int, + default=0, + required=False, + help="starting number of FRBs to analyze", +) +parser.add_argument( + "-o", "--opfile", type=str, required=False, help="Output file for the data" +) +parser.add_argument( + "--survey", + type=str, + default="CRACO_std_May2022", + required=False, + help="Survey name", +) +parser.add_argument( + "--lum_func", + type=int, + default=0, + required=False, + help="Luminosity function (0=power-law, 1=gamma)", +) pargs = parser.parse_args() main(pargs) -''' +""" # OUT OF DATE TESTS python test_with_craco.py sfr_n 0.2 2. --nstep 100 --nFRB 1000 --cosmo Planck15 -o CRACO_1000_sfr_n.png python test_with_craco.py gamma -1.5 -0.8 --nstep 30 --nFRB 1000 --cosmo Planck15 -o CRACO_1000_gamma.png @@ -167,4 +198,17 @@ def main(pargs): # python testing.py H0 60. 80. --nstep 50 --nFRB 100 -o MC_Plots/CRACO_100_H0.png --lum_func 2 -''' \ No newline at end of file + +# +python testing.py F .001 .1 --nstep 100 --nFRB 1000 -o MC_F/Plots/synth_100_F_0.01.png --lum_func 2 --survey ../MC_F/Surveys/F_0.01_survey +python testing.py F .1 .999 --nstep 100 --nFRB 1000 -o MC_F/Plots/synth_100_F_0.7.png --lum_func 2 --survey ../MC_F/Surveys/F_0.7_survey +python testing.py F .1 .999 --nstep 100 --nFRB 1000 -o MC_F/Plots/synth_100_F_0.9.png --lum_func 2 --survey ../MC_F/Surveys/F_0.9_survey +python testing.py F .1 .999 --nstep 100 --nFRB 1000 -o MC_F/Plots/synth_100_F_vanilla.png --lum_func 2 --survey ../MC_F/Surveys/F_vanilla_survey + +python testing.py F .001 .1 --nstep 100 --nFRB 1000 -o MC_F/Plots/synth_100_F_dmhost_suppressed_0.01.png --lum_func 2 --survey ../MC_F/Surveys/F_0.01_dmhost_suppressed_survey +python testing.py F .1 .999 --nstep 100 --nFRB 1000 -o MC_F/Plots/synth_100_F_dmhost_suppressed_0.7.png --lum_func 2 --survey ../MC_F/Surveys/F_0.7_dmhost_suppressed_survey +python testing.py F .1 .999 --nstep 100 --nFRB 1000 -o MC_F/Plots/synth_100_F_dmhost_suppressed_0.9.png --lum_func 2 --survey ../MC_F/Surveys/F_0.9_dmhost_suppressed_survey +python testing.py F .1 .999 --nstep 100 --nFRB 1000 -o MC_F/Plots/synth_100_F_dmhost_suppressed_vanilla.png --lum_func 2 --survey ../MC_F/Surveys/F_vanilla_dmhost_suppressed_survey + +""" + diff --git a/zdm/misc_functions.py b/zdm/misc_functions.py index b31432ba..52304d4e 100644 --- a/zdm/misc_functions.py +++ b/zdm/misc_functions.py @@ -2044,13 +2044,13 @@ def plot_grid_2(zDMgrid,zvals,dmvals, dz=zvals[1]-zvals[0] if norm==1: zDMgrid /= ddm - if Aconts: - alevels /= ddm + # if Aconts: + # alevels /= ddm elif norm==2: xnorm=np.sum(zDMgrid) zDMgrid /= xnorm - if Aconts: - alevels /= xnorm + # if Aconts: + # alevels /= xnorm elif norm==3: zDMgrid /= np.max(zDMgrid) @@ -2072,6 +2072,11 @@ def plot_grid_2(zDMgrid,zvals,dmvals, iwhich=np.where(cslist > ac)[0][0] alevels[i]=slist[iwhich] + if norm == 1: + alevels /= ddm + elif norm == 2: + alevels /= xnorm + ### generates contours *before* cutting array in DM ### ### might need to normalise contours by integer lengths, oh well! ### if conts: From d8e8b35593cb92c245829cf47fe8e02f984bf789 Mon Sep 17 00:00:00 2001 From: Jay-MBP Date: Sat, 9 Jul 2022 13:54:58 -0700 Subject: [PATCH 009/104] Add F parameter to minicube configuration file --- .../Analysis/CRACO/Cubes/craco_mini_cube.json | 50 ++++++++++++------- 1 file changed, 33 insertions(+), 17 deletions(-) diff --git a/papers/F/Analysis/CRACO/Cubes/craco_mini_cube.json b/papers/F/Analysis/CRACO/Cubes/craco_mini_cube.json index a4271c13..1f887129 100644 --- a/papers/F/Analysis/CRACO/Cubes/craco_mini_cube.json +++ b/papers/F/Analysis/CRACO/Cubes/craco_mini_cube.json @@ -11,54 +11,70 @@ } }, "cube": { - "parameter_order": ["lC", "sfr_n", "alpha", "lEmax", "lmean", "lsigma", "gamma", "H0"] + "parameter_order": [ + "lC", + "sfr_n", + "alpha", + "lEmax", + "lmean", + "lsigma", + "gamma", + "H0", + "F" + ] }, "lEmax": { "DC": "energy", - "min": 40.5, - "max": 42.5, + "min": 40.5, + "max": 42.5, "n": 50 }, "H0": { "DC": "cosmo", - "min": 55.0, - "max": 80.0, + "min": 55.0, + "max": 80.0, "n": 25 }, "alpha": { "DC": "energy", - "min": 0.2, - "max": 2.0, + "min": 0.2, + "max": 2.0, "n": 3 }, "gamma": { "DC": "energy", - "min": -0.5, - "max": -1.5, + "min": -0.5, + "max": -1.5, "n": 10 }, "sfr_n": { "DC": "FRBdemo", - "min": 0.0, - "max": 3.0, + "min": 0.0, + "max": 3.0, "n": 20 }, "lmean": { "DC": "host", - "min": 1.7, - "max": 2.5, + "min": 1.7, + "max": 2.5, "n": 5 }, "lsigma": { "DC": "host", - "min": 0.3, - "max": 0.7, + "min": 0.3, + "max": 0.7, "n": 5 }, "lC": { "DC": "FRBdemo", - "min": -0.911, - "max": -0.911, + "min": -0.911, + "max": -0.911, "n": -1 + }, + "F": { + "DC": "IGM", + "min": 0.01, + "max": 0.99, + "n": 50 } } \ No newline at end of file From e8a40fcea0999551731494216a2b096dfb10d4bd Mon Sep 17 00:00:00 2001 From: Jay-MBP Date: Wed, 13 Jul 2022 09:11:22 -0700 Subject: [PATCH 010/104] adjust minicube parameters to shorten lux run --- .../F/Analysis/CRACO/Cloud/run_craco_mini.py | 78 +++++++++++++------ 1 file changed, 54 insertions(+), 24 deletions(-) diff --git a/papers/F/Analysis/CRACO/Cloud/run_craco_mini.py b/papers/F/Analysis/CRACO/Cloud/run_craco_mini.py index c0a5e9d8..dc992b73 100644 --- a/papers/F/Analysis/CRACO/Cloud/run_craco_mini.py +++ b/papers/F/Analysis/CRACO/Cloud/run_craco_mini.py @@ -14,20 +14,27 @@ from IPython import embed -def main(pargs, pfile:str, oproot:str, NFRB:int=None, iFRB:int=0, - outdir:str='Output'): + +def main( + pargs, + pfile: str, + oproot: str, + NFRB: int = None, + iFRB: int = 0, + outdir: str = "Output", +): # Generate the folder? if not os.path.isdir(outdir): os.mkdir(outdir) - + ############## Load up ############## - input_dict=io.process_jfile(pfile) + input_dict = io.process_jfile(pfile) # Deconstruct the input_dict state_dict, cube_dict, vparam_dict = it.parse_input_dict(input_dict) - npoints = np.array([item['n'] for key, item in vparam_dict.items()]) + npoints = np.array([item["n"] for key, item in vparam_dict.items()]) ntotal = int(np.prod(np.abs(npoints))) # Total number of CPUs to be running on this Cube @@ -35,30 +42,38 @@ def main(pargs, pfile:str, oproot:str, NFRB:int=None, iFRB:int=0, batch = 1 if pargs.batch is None else pargs.batch nper_cpu = ntotal // total_ncpu - if int(ntotal/total_ncpu) != nper_cpu: + if int(ntotal / total_ncpu) != nper_cpu: raise IOError(f"Ncpu={total_ncpu} must divide evenly into ntotal={ntotal}") commands = [] for kk in range(pargs.ncpu): line = [] # Which CPU is running out of the total? - iCPU = (batch-1)*pargs.ncpu + kk - outfile = os.path.join(outdir, oproot.replace('.csv', f'{iCPU+1}.csv')) + iCPU = (batch - 1) * pargs.ncpu + kk + outfile = os.path.join(outdir, oproot.replace(".csv", f"{iCPU+1}.csv")) # Command - line = ['zdm_build_cube', - '-n', f'{iCPU+1}', - '-m', f'{nper_cpu}', - '-o', f'{outfile}', - '-s', f'CRACO_alpha1_Planck18_Gamma', '--clobber', - '-p', f'{pfile}'] + line = [ + "zdm_build_cube", + "-n", + f"{iCPU+1}", + "-m", + f"{nper_cpu}", + "-o", + f"{outfile}", + "-s", + f"CRACO_alpha1_Planck18_Gamma", + "--clobber", + "-p", + f"{pfile}", + ] # NFRB? if NFRB is not None: - line += [f'--NFRB', f'{NFRB}'] + line += [f"--NFRB", f"{NFRB}"] # iFRB? if iFRB > 0: - line += [f'--iFRB', f'{iFRB}'] + line += [f"--iFRB", f"{iFRB}"] # Finish - #line += ' & \n' + # line += ' & \n' commands.append(line) # Launch em! @@ -75,14 +90,29 @@ def main(pargs, pfile:str, oproot:str, NFRB:int=None, iFRB:int=0, print("All done!") + def parse_option(): # test for command-line arguments here parser = argparse.ArgumentParser() - parser.add_argument('-n','--ncpu',type=int, required=True,help="Number of CPUs to run on (might be split in batches)") - parser.add_argument('-t','--total_ncpu',type=int, required=False,help="Total number of CPUs to run on (might be split in batches)") - parser.add_argument('-b','--batch',type=int, default=1, required=False,help="Batch number") - #parser.add_argument('--NFRB',type=int,required=False,help="Number of FRBs to analzye") - #parser.add_argument('--iFRB',type=int,default=0,help="Initial FRB to run from") + parser.add_argument( + "-n", + "--ncpu", + type=int, + required=True, + help="Number of CPUs to run on (might be split in batches)", + ) + parser.add_argument( + "-t", + "--total_ncpu", + type=int, + required=False, + help="Total number of CPUs to run on (might be split in batches)", + ) + parser.add_argument( + "-b", "--batch", type=int, default=1, required=False, help="Batch number" + ) + # parser.add_argument('--NFRB',type=int,required=False,help="Number of FRBs to analzye") + # parser.add_argument('--iFRB',type=int,default=0,help="Initial FRB to run from") args = parser.parse_args() return args @@ -90,7 +120,7 @@ def parse_option(): if __name__ == "__main__": # get the argument of training. - pfile = '../Cubes/craco_mini_cube.json' - oproot = 'craco_mini.csv' + pfile = "../Cubes/craco_mini_cube.json" + oproot = "craco_mini.csv" pargs = parse_option() main(pargs, pfile, oproot, NFRB=100, iFRB=100) From 751594d0d80d3ce404949124f95ac29ab915d4f4 Mon Sep 17 00:00:00 2001 From: Jay-MBP Date: Wed, 13 Jul 2022 09:13:57 -0700 Subject: [PATCH 011/104] adjust minicube for a shorter lux run --- papers/F/Analysis/CRACO/Cubes/craco_mini_cube.json | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/papers/F/Analysis/CRACO/Cubes/craco_mini_cube.json b/papers/F/Analysis/CRACO/Cubes/craco_mini_cube.json index 1f887129..d4816c1a 100644 --- a/papers/F/Analysis/CRACO/Cubes/craco_mini_cube.json +++ b/papers/F/Analysis/CRACO/Cubes/craco_mini_cube.json @@ -18,16 +18,16 @@ "lEmax", "lmean", "lsigma", + "F", "gamma", - "H0", - "F" + "H0" ] }, "lEmax": { "DC": "energy", "min": 40.5, "max": 42.5, - "n": 50 + "n": 10 }, "H0": { "DC": "cosmo", @@ -45,7 +45,7 @@ "DC": "energy", "min": -0.5, "max": -1.5, - "n": 10 + "n": 5 }, "sfr_n": { "DC": "FRBdemo", @@ -75,6 +75,6 @@ "DC": "IGM", "min": 0.01, "max": 0.99, - "n": 50 + "n": 10 } } \ No newline at end of file From d629cfa93e98e1b0c8f7c3c385f9571af478493e Mon Sep 17 00:00:00 2001 From: profxj Date: Wed, 13 Jul 2022 11:52:39 -0700 Subject: [PATCH 012/104] slurp --- .../F/Analysis/CRACO/py/slurp_craco_cubes.py | 126 ++++++++++++++++++ 1 file changed, 126 insertions(+) create mode 100644 papers/F/Analysis/CRACO/py/slurp_craco_cubes.py diff --git a/papers/F/Analysis/CRACO/py/slurp_craco_cubes.py b/papers/F/Analysis/CRACO/py/slurp_craco_cubes.py new file mode 100644 index 00000000..406b701e --- /dev/null +++ b/papers/F/Analysis/CRACO/py/slurp_craco_cubes.py @@ -0,0 +1,126 @@ +""" Simple script to slurp """ + +from zdm import analyze_cube + + +def main(pargs): + + if pargs.run == 'Emax': + # Emax + input_file = 'Cubes/craco_H0_Emax_cube.json' + prefix = 'Cubes/tmp' + nsurveys = 1 + + # Run it + analyze_cube.slurp_cube(input_file, prefix, 'Cubes/craco_H0_Emax_cube.npz', + nsurveys) + + elif pargs.run == 'F': + # Emax + input_file = 'Cubes/craco_H0_F_cube.json' + prefix = 'Cubes/craco_H0_F_cube' + nsurveys = 1 + + # Run it + analyze_cube.slurp_cube(input_file, prefix, + 'Cubes/craco_H0_F_cube.npz', + nsurveys) + elif pargs.run == 'mini': + # Emax + input_file = 'Cubes/craco_mini_cube.json' + prefix = 'Cubes/craco_mini' + nsurveys = 1 + + # Run it + analyze_cube.slurp_cube(input_file, prefix, + 'Cubes/craco_mini_cube.npz', + nsurveys) + elif pargs.run == 'submini': + # Emax + input_file = 'Cubes/craco_submini_cube.json' + prefix = 'Cubes/craco_submini_cube' + nsurveys = 1 + + # Run it + analyze_cube.slurp_cube(input_file, prefix, + 'Cubes/craco_submini_cube.npz', + nsurveys) + + elif pargs.run == 'sfrEmax': + # Emax + input_file = 'Cubes/craco_sfr_Emax_cube.json' + prefix = 'Cubes/craco_sfr_Emax_cube' + nsurveys = 1 + + # Run it + analyze_cube.slurp_cube(input_file, prefix, + 'Cubes/craco_sfr_Emax_cube.npz', + nsurveys) + + elif pargs.run == 'alphaEmax': + # Emax + input_file = 'Cubes/craco_alpha_Emax_cube.json' + prefix = 'Cubes/craco_alpha_Emax_cube' + nsurveys = 1 + + # Run it + analyze_cube.slurp_cube(input_file, prefix, + 'Cubes/craco_alpha_Emax_cube.npz', + nsurveys) + elif pargs.run == 'full': + # Emax + input_file = 'Cubes/craco_full_cube.json' + prefix = 'Cubes/craco_full' + nsurveys = 1 + + # Run it + analyze_cube.slurp_cube(input_file, prefix, + 'Cubes/craco_full_cube.npz', + nsurveys) + elif pargs.run == 'another_full': + # Emax + input_file = 'Cubes/craco_full_cube.json' + prefix = 'Cubes/craco_400_full' + nsurveys = 1 + + # Run it + analyze_cube.slurp_cube(input_file, prefix, + 'Cubes/craco_400_full_cube.npz', + nsurveys) + elif pargs.run == 'third_full': + # Emax + input_file = 'Cubes/craco_full_cube.json' + prefix = 'Cubes/craco_3rd_full' + nsurveys = 1 + + # Run it + analyze_cube.slurp_cube(input_file, prefix, + 'Cubes/craco_3rd_full_cube.npz', + nsurveys) + + +def parse_option(): + """ + This is a function used to parse the arguments in the training. + + Returns: + args: (dict) dictionary of the arguments. + """ + import argparse + + parser = argparse.ArgumentParser("Slurping the cubes") + parser.add_argument("run", type=str, help="Run to slurp") + #parser.add_argument('--debug', default=False, action='store_true', + # help='Debug?') + args = parser.parse_args() + + return args + +# Command line execution +if __name__ == '__main__': + + pargs = parse_option() + main(pargs) + +# python py/slurp_craco_cubes.py mini +# python py/slurp_craco_cubes.py another_full From f1c953d19dfa3ea0552ea897d341eac889643ede Mon Sep 17 00:00:00 2001 From: profxj Date: Wed, 13 Jul 2022 11:55:37 -0700 Subject: [PATCH 013/104] qck explore --- .../F/Analysis/CRACO/py/craco_qck_explore.py | 86 +++++++++++++++++++ 1 file changed, 86 insertions(+) create mode 100644 papers/F/Analysis/CRACO/py/craco_qck_explore.py diff --git a/papers/F/Analysis/CRACO/py/craco_qck_explore.py b/papers/F/Analysis/CRACO/py/craco_qck_explore.py new file mode 100644 index 00000000..2dc4c9ba --- /dev/null +++ b/papers/F/Analysis/CRACO/py/craco_qck_explore.py @@ -0,0 +1,86 @@ +# imports +from importlib import reload +import numpy as np +import sys, os + +from zdm import analyze_cube +from zdm import iteration as it +from zdm import io +from zdm.craco import loading + + +#sys.path.append(os.path.abspath("../../Figures/py")) + +def main(pargs): + jroot = None + if pargs.run == 'mini': + scube = 'mini' + outdir = 'Mini/' + elif pargs.run == 'full': + scube = 'full' + outdir = 'Full/' + elif pargs.run == 'full400': + scube = '400_full' + jroot = 'full' + outdir = 'Full400/' + elif pargs.run == 'full3rd': + scube = '3rd_full' + jroot = 'full' + outdir = 'Full3rd/' + + if jroot is None: + jroot = scube + + + # Load + npdict = np.load(f'Cubes/craco_{scube}_cube.npz') + + ll_cube = npdict['ll'] + + # Deal with Nan + ll_cube[np.isnan(ll_cube)] = -1e99 + params = npdict['params'] + + # Cube parameters + ############## Load up ############## + pfile = f'Cubes/craco_{jroot}_cube.json' + input_dict=io.process_jfile(pfile) + + # Deconstruct the input_dict + state_dict, cube_dict, vparam_dict = it.parse_input_dict(input_dict) + + # Run Bayes + + # Offset by max + ll_cube = ll_cube - np.max(ll_cube) + + uvals,vectors,wvectors = analyze_cube.get_bayesian_data(ll_cube) + + analyze_cube.do_single_plots(uvals,vectors,wvectors, params, + vparams_dict=vparam_dict, outdir=outdir) + print(f"Wrote figures to {outdir}") + +def parse_option(): + """ + This is a function used to parse the arguments in the training. + + Returns: + args: (dict) dictionary of the arguments. + """ + import argparse + + parser = argparse.ArgumentParser("Slurping the cubes") + parser.add_argument("run", type=str, help="Run to slurp") + #parser.add_argument('--debug', default=False, action='store_true', + # help='Debug?') + args = parser.parse_args() + + return args + +# Command line execution +if __name__ == '__main__': + + pargs = parse_option() + main(pargs) + +# python py/slurp_craco_cubes.py mini \ No newline at end of file From 071e69ad3981a1c61a0cb14b2180f1315c00150a Mon Sep 17 00:00:00 2001 From: Jay-MBP Date: Fri, 15 Jul 2022 13:23:41 -0700 Subject: [PATCH 014/104] fix bugs in slurp code, prep for a F=0.32 cube --- .../F/Analysis/CRACO/py/craco_qck_explore.py | 4 + .../F/Analysis/CRACO/py/slurp_craco_cubes.py | 115 +- zdm/analyze_cube.py | 1377 ++++++++++------- zdm/craco/mc.py | 108 +- zdm/craco/testing.py | 11 +- 5 files changed, 920 insertions(+), 695 deletions(-) diff --git a/papers/F/Analysis/CRACO/py/craco_qck_explore.py b/papers/F/Analysis/CRACO/py/craco_qck_explore.py index 2dc4c9ba..748fb812 100644 --- a/papers/F/Analysis/CRACO/py/craco_qck_explore.py +++ b/papers/F/Analysis/CRACO/py/craco_qck_explore.py @@ -8,6 +8,8 @@ from zdm import io from zdm.craco import loading +from IPython import embed + #sys.path.append(os.path.abspath("../../Figures/py")) @@ -56,6 +58,8 @@ def main(pargs): uvals,vectors,wvectors = analyze_cube.get_bayesian_data(ll_cube) + embed(header="Debugging...") + analyze_cube.do_single_plots(uvals,vectors,wvectors, params, vparams_dict=vparam_dict, outdir=outdir) print(f"Wrote figures to {outdir}") diff --git a/papers/F/Analysis/CRACO/py/slurp_craco_cubes.py b/papers/F/Analysis/CRACO/py/slurp_craco_cubes.py index 406b701e..e8d6087a 100644 --- a/papers/F/Analysis/CRACO/py/slurp_craco_cubes.py +++ b/papers/F/Analysis/CRACO/py/slurp_craco_cubes.py @@ -5,98 +5,100 @@ def main(pargs): - if pargs.run == 'Emax': + if pargs.run == "Emax": # Emax - input_file = 'Cubes/craco_H0_Emax_cube.json' - prefix = 'Cubes/tmp' + input_file = "Cubes/craco_H0_Emax_cube.json" + prefix = "Cubes/tmp" nsurveys = 1 # Run it - analyze_cube.slurp_cube(input_file, prefix, 'Cubes/craco_H0_Emax_cube.npz', - nsurveys) + analyze_cube.slurp_cube( + input_file, prefix, "Cubes/craco_H0_Emax_cube.npz", nsurveys + ) - elif pargs.run == 'F': + elif pargs.run == "F": # Emax - input_file = 'Cubes/craco_H0_F_cube.json' - prefix = 'Cubes/craco_H0_F_cube' + input_file = "Cubes/craco_H0_F_cube.json" + prefix = "Cubes/craco_H0_F_cube" nsurveys = 1 # Run it - analyze_cube.slurp_cube(input_file, prefix, - 'Cubes/craco_H0_F_cube.npz', - nsurveys) - elif pargs.run == 'mini': + analyze_cube.slurp_cube( + input_file, prefix, "Cubes/craco_H0_F_cube.npz", nsurveys + ) + elif pargs.run == "mini": # Emax - input_file = 'Cubes/craco_mini_cube.json' - prefix = 'Cubes/craco_mini' + input_file = "Cubes/craco_mini_cube.json" + # prefix = 'Cubes/craco_mini' + prefix = "Cloud/Output/craco_mini" nsurveys = 1 # Run it - analyze_cube.slurp_cube(input_file, prefix, - 'Cubes/craco_mini_cube.npz', - nsurveys) - elif pargs.run == 'submini': + analyze_cube.slurp_cube( + input_file, prefix, "Cubes/craco_mini_cube.npz", nsurveys + ) + elif pargs.run == "submini": # Emax - input_file = 'Cubes/craco_submini_cube.json' - prefix = 'Cubes/craco_submini_cube' + input_file = "Cubes/craco_submini_cube.json" + prefix = "Cubes/craco_submini_cube" nsurveys = 1 # Run it - analyze_cube.slurp_cube(input_file, prefix, - 'Cubes/craco_submini_cube.npz', - nsurveys) + analyze_cube.slurp_cube( + input_file, prefix, "Cubes/craco_submini_cube.npz", nsurveys + ) - elif pargs.run == 'sfrEmax': + elif pargs.run == "sfrEmax": # Emax - input_file = 'Cubes/craco_sfr_Emax_cube.json' - prefix = 'Cubes/craco_sfr_Emax_cube' + input_file = "Cubes/craco_sfr_Emax_cube.json" + prefix = "Cubes/craco_sfr_Emax_cube" nsurveys = 1 # Run it - analyze_cube.slurp_cube(input_file, prefix, - 'Cubes/craco_sfr_Emax_cube.npz', - nsurveys) + analyze_cube.slurp_cube( + input_file, prefix, "Cubes/craco_sfr_Emax_cube.npz", nsurveys + ) - elif pargs.run == 'alphaEmax': + elif pargs.run == "alphaEmax": # Emax - input_file = 'Cubes/craco_alpha_Emax_cube.json' - prefix = 'Cubes/craco_alpha_Emax_cube' + input_file = "Cubes/craco_alpha_Emax_cube.json" + prefix = "Cubes/craco_alpha_Emax_cube" nsurveys = 1 # Run it - analyze_cube.slurp_cube(input_file, prefix, - 'Cubes/craco_alpha_Emax_cube.npz', - nsurveys) - elif pargs.run == 'full': + analyze_cube.slurp_cube( + input_file, prefix, "Cubes/craco_alpha_Emax_cube.npz", nsurveys + ) + elif pargs.run == "full": # Emax - input_file = 'Cubes/craco_full_cube.json' - prefix = 'Cubes/craco_full' + input_file = "Cubes/craco_full_cube.json" + prefix = "Cubes/craco_full" nsurveys = 1 # Run it - analyze_cube.slurp_cube(input_file, prefix, - 'Cubes/craco_full_cube.npz', - nsurveys) - elif pargs.run == 'another_full': + analyze_cube.slurp_cube( + input_file, prefix, "Cubes/craco_full_cube.npz", nsurveys + ) + elif pargs.run == "another_full": # Emax - input_file = 'Cubes/craco_full_cube.json' - prefix = 'Cubes/craco_400_full' + input_file = "Cubes/craco_full_cube.json" + prefix = "Cubes/craco_400_full" nsurveys = 1 # Run it - analyze_cube.slurp_cube(input_file, prefix, - 'Cubes/craco_400_full_cube.npz', - nsurveys) - elif pargs.run == 'third_full': + analyze_cube.slurp_cube( + input_file, prefix, "Cubes/craco_400_full_cube.npz", nsurveys + ) + elif pargs.run == "third_full": # Emax - input_file = 'Cubes/craco_full_cube.json' - prefix = 'Cubes/craco_3rd_full' + input_file = "Cubes/craco_full_cube.json" + prefix = "Cubes/craco_3rd_full" nsurveys = 1 # Run it - analyze_cube.slurp_cube(input_file, prefix, - 'Cubes/craco_3rd_full_cube.npz', - nsurveys) + analyze_cube.slurp_cube( + input_file, prefix, "Cubes/craco_3rd_full_cube.npz", nsurveys + ) def parse_option(): @@ -110,14 +112,15 @@ def parse_option(): parser = argparse.ArgumentParser("Slurping the cubes") parser.add_argument("run", type=str, help="Run to slurp") - #parser.add_argument('--debug', default=False, action='store_true', + # parser.add_argument('--debug', default=False, action='store_true', # help='Debug?') args = parser.parse_args() - + return args + # Command line execution -if __name__ == '__main__': +if __name__ == "__main__": pargs = parse_option() main(pargs) diff --git a/zdm/analyze_cube.py b/zdm/analyze_cube.py index 80d910e4..83bd3a66 100644 --- a/zdm/analyze_cube.py +++ b/zdm/analyze_cube.py @@ -16,9 +16,15 @@ from IPython import embed -def slurp_cube(input_file:str, prefix:str, outfile:str, - nsurveys, debug:bool=False, - suffix:str='.csv'): + +def slurp_cube( + input_file: str, + prefix: str, + outfile: str, + nsurveys, + debug: bool = False, + suffix: str = ".csv", +): """ Slurp the cube ASCII output files and write lC and ll into a numpy savez file @@ -31,18 +37,18 @@ def slurp_cube(input_file:str, prefix:str, outfile:str, suffix (str, optional): File ending. Allows for old .out files """ # Grab em. The order doesn't matter - files = glob.glob(prefix+'*'+suffix) + files = glob.glob(prefix + "*" + suffix) # Init - input_dict=io.process_jfile(input_file) + input_dict = io.process_jfile(input_file) _, cube_dict, vparam_dict = iteration.parse_input_dict(input_dict) PARAMS = list(vparam_dict.keys()) # Prep - order, iorder = iteration.set_orders(cube_dict['parameter_order'], PARAMS) + order, iorder = iteration.set_orders(cube_dict["parameter_order"], PARAMS) cube_shape = iteration.set_cube_shape(vparam_dict, order) - param_shape = np.array([0]+cube_shape)[iorder].tolist()[:-1] + param_shape = np.array([1] + cube_shape)[iorder].tolist()[:-1] # Outputs ll_cube = np.zeros(param_shape, dtype=np.float32) @@ -54,12 +60,12 @@ def slurp_cube(input_file:str, prefix:str, outfile:str, pDMz_cube = np.zeros(param_shape, dtype=np.float32) pz_cube = np.zeros(param_shape, dtype=np.float32) - survey_items = ['lls', 'P_zDM', 'P_n', 'P_s', 'N'] - names = ['icube'] + PARAMS - for ss in range (nsurveys): - names += [item+f'_{ss}' for item in survey_items] - names += ['ll'] - + survey_items = ["lls", "P_zDM", "P_n", "P_s", "N"] + names = ["icube"] + PARAMS + for ss in range(nsurveys): + names += [item + f"_{ss}" for item in survey_items] + names += ["ll"] + # Loop on cube output files survey_arrays = {} nsurvey = 0 @@ -72,10 +78,10 @@ def slurp_cube(input_file:str, prefix:str, outfile:str, if ss == 0: # Count the surveys for key in df.keys(): - if 'P_zDM' in key and len(key) > len('P_zDM'): + if "P_zDM" in key and len(key) > len("P_zDM"): # Generate them for item in survey_items: - survey_arrays[item+key[-1]] = np.zeros(param_shape) + survey_arrays[item + key[-1]] = np.zeros(param_shape) nsurvey += 1 # Get indices @@ -83,10 +89,12 @@ def slurp_cube(input_file:str, prefix:str, outfile:str, ns = df.n for n in ns: - r_current = np.array([0]+list(np.unravel_index( - int(n), cube_shape, order='F'))) - current = r_current[iorder][:-1] # Truncate lC + r_current = np.array( + [0] + list(np.unravel_index(int(n), cube_shape, order="F")) + ) + current = r_current[iorder][:-1] # Truncate lC # Ravel me back + idx = np.ravel_multi_index(current, ll_cube.shape) indices.append(idx) @@ -105,35 +113,39 @@ def slurp_cube(input_file:str, prefix:str, outfile:str, # Check if debug: - embed(header='69 of analyze') - + embed(header="69 of analyze") + # Grids - out_dict = dict(ll=ll_cube, - lC=lC_cube, - params=PARAMS[:-1], - pzDM=pzDM_cube, - pDM=pDM_cube, - pDMz=pDMz_cube, - pz=pz_cube) + out_dict = dict( + ll=ll_cube, + lC=lC_cube, + params=PARAMS[:-1], + pzDM=pzDM_cube, + pDM=pDM_cube, + pDMz=pDMz_cube, + pz=pz_cube, + ) + + embed(header="line 129") + # Save the parameter values too for name in PARAMS[:-1]: - out_dict[name] = np.linspace(vparam_dict[name]['min'], - vparam_dict[name]['max'], - vparam_dict[name]['n']) + + out_dict[name] = np.linspace( + vparam_dict[name]["min"], vparam_dict[name]["max"], vparam_dict[name]["n"] + ) # Survey items for key in survey_arrays.keys(): out_dict[key] = survey_arrays[key] # Write - np.savez(outfile, **out_dict) #ll=ll_cube, lC=lC_cube, params=PARAMS[-1]) + np.savez(outfile, **out_dict) # ll=ll_cube, lC=lC_cube, params=PARAMS[-1]) print(f"Wrote: {outfile}") -def apply_gaussian_prior(lls:np.ndarray, - iparam:int, - values:np.ndarray, - mean:float, - sigma:float): +def apply_gaussian_prior( + lls: np.ndarray, iparam: int, values: np.ndarray, mean: float, sigma: float +): """ Applies a prior to parameter iparam with mean mean and deviation sigma. @@ -141,31 +153,41 @@ def apply_gaussian_prior(lls:np.ndarray, Returns a vector of length lls modified by that prior. """ - NDIMS= len(lls.shape) + NDIMS = len(lls.shape) if iparam < 0 or iparam >= NDIMS: - raise ValueError("Data only has ",NDIMS," dimensions.", - "Please select iparam between 0 and ",NDIMS-1," not ",iparam) - + raise ValueError( + "Data only has ", + NDIMS, + " dimensions.", + "Please select iparam between 0 and ", + NDIMS - 1, + " not ", + iparam, + ) + wlls = np.copy(lls) - - for iv,val in enumerate(values): + + for iv, val in enumerate(values): # select ivth value from iparam dimension - big_slice = [slice(None,None,None)]*NDIMS + big_slice = [slice(None, None, None)] * NDIMS big_slice[iparam] = iv - - #calculate weights. Yes I know this is silly. - weight = np.exp(-0.5*((val-mean)/sigma)**2) + + # calculate weights. Yes I know this is silly. + weight = np.exp(-0.5 * ((val - mean) / sigma) ** 2) weight = np.log10(weight) wlls[tuple(big_slice)] += weight return wlls -def apply_H0_prior(lls:np.ndarray, - H0dim:int, - H0values:np.ndarray, - cmbH0:float, - cmb_sigma:float, - sn1aH0:float, - sn1a_sigma:float): + +def apply_H0_prior( + lls: np.ndarray, + H0dim: int, + H0values: np.ndarray, + cmbH0: float, + cmb_sigma: float, + sn1aH0: float, + sn1a_sigma: float, +): """ Applies a prior as a function of H0 @@ -190,44 +212,52 @@ def apply_H0_prior(lls:np.ndarray, Returns a vector of length lls modified by that prior. """ - + # orders limits appropriately if cmbH0 < sn1aH0: - lowH0=cmbH0 - lowsigma=cmb_sigma - highH0=sn1aH0 - highsigma=sn1a_sigma + lowH0 = cmbH0 + lowsigma = cmb_sigma + highH0 = sn1aH0 + highsigma = sn1a_sigma else: - lowH0=sn1aH0 - lowsigma=sn1a_sigma - highH0=cmbH0 - highsigma=cmb_sigma - - NDIMS= len(lls.shape) + lowH0 = sn1aH0 + lowsigma = sn1a_sigma + highH0 = cmbH0 + highsigma = cmb_sigma + + NDIMS = len(lls.shape) if H0dim < 0 or H0dim >= NDIMS: - raise ValueError("Data only has ",NDIMS," dimensions.", - "Please select H0dim between 0 and ",NDIMS-1," not ",H0dim) - + raise ValueError( + "Data only has ", + NDIMS, + " dimensions.", + "Please select H0dim between 0 and ", + NDIMS - 1, + " not ", + H0dim, + ) + wlls = np.copy(lls) - - for iv,val in enumerate(H0values): + + for iv, val in enumerate(H0values): # select ivth value from iparam dimension - big_slice = [slice(None,None,None)]*NDIMS + big_slice = [slice(None, None, None)] * NDIMS big_slice[H0dim] = iv - - #calculate weights. + + # calculate weights. if val < lowH0: - weight = -0.5*((val-lowH0)/lowsigma)**2 * np.log10(np.exp(1)) + weight = -0.5 * ((val - lowH0) / lowsigma) ** 2 * np.log10(np.exp(1)) elif val > highH0: - weight = -0.5*((val-highH0)/highsigma)**2 * np.log10(np.exp(1)) + weight = -0.5 * ((val - highH0) / highsigma) ** 2 * np.log10(np.exp(1)) else: - weight=0. - + weight = 0.0 + wlls[tuple(big_slice)] += weight - + return wlls -def get_slice_from_parameters(data,plist,mcvals,verbose=False,wanted="ll"): + +def get_slice_from_parameters(data, plist, mcvals, verbose=False, wanted="ll"): """ Selects from data according to parameters which are as close to those in the mcvals as possible. @@ -242,50 +272,57 @@ def get_slice_from_parameters(data,plist,mcvals,verbose=False,wanted="ll"): Returns: Array of NDIM=dim(data)-dim(plist) likelihood values """ - NDIMS=len(data["params"]) + NDIMS = len(data["params"]) if verbose: - print("We have ",NDIMS," dimensions to this cube") - + print("We have ", NDIMS, " dimensions to this cube") + # sets up array of which values to keep - which=np.full([NDIMS],-1) - + which = np.full([NDIMS], -1) + # finds the order of these parameters in the multidimensional cube - iplist=[] + iplist = [] for param in plist: iplist.append(np.where(data["params"] == param)[0][0]) - + # identifies the closest parameter values - iclosest=[] - for i,param in enumerate(plist): - vals=data[param] - vals=np.array(vals) - diffs=(mcvals[i]-vals)**2 - imindiff=np.argmin(diffs) + iclosest = [] + for i, param in enumerate(plist): + vals = data[param] + vals = np.array(vals) + diffs = (mcvals[i] - vals) ** 2 + imindiff = np.argmin(diffs) iclosest.append(imindiff) if verbose: - print('For ',param,' MC truth ',mcvals[i]," closest cube value is the ", - imindiff,"th, with value ",vals[imindiff]) - which[iplist[i]]=imindiff - + print( + "For ", + param, + " MC truth ", + mcvals[i], + " closest cube value is the ", + imindiff, + "th, with value ", + vals[imindiff], + ) + which[iplist[i]] = imindiff + # selects the slice while keeping all MC parameters constant - big_slice = [slice(None,None,None)]*NDIMS + big_slice = [slice(None, None, None)] * NDIMS for i in np.arange(NDIMS): - if which[i] >= 0: #insert code of -1 for "leave free" + if which[i] >= 0: # insert code of -1 for "leave free" big_slice[i] = which[i] if isinstance(wanted, list): - for i,item in enumerate(wanted): - if i==0: - selection=data[item][tuple(big_slice)] + for i, item in enumerate(wanted): + if i == 0: + selection = data[item][tuple(big_slice)] else: selection += data[item][tuple(big_slice)] else: - selection=data[wanted][tuple(big_slice)] + selection = data[wanted][tuple(big_slice)] selection = np.array(selection) return selection -def get_bayesian_data(lls:np.ndarray, - plls:np.ndarray=None, - pklfile=None): + +def get_bayesian_data(lls: np.ndarray, plls: np.ndarray = None, pklfile=None): """ Method to perform simple Bayesian analysis on the Log-likelihood cube @@ -300,102 +337,101 @@ def get_bayesian_data(lls:np.ndarray, lists of np.ndarray's of LL analysis One item per parameter in the cube """ - NDIMS= len(lls.shape) - + NDIMS = len(lls.shape) + # multiplies all log-likelihoods by the maximum value # ensures no floating point problems global_max = np.nanmax(lls) lls -= global_max - - #eventually remove this line + + # eventually remove this line if plls is None: plls = lls - - origlls=lls - + + origlls = lls + if plls is not None: w_global_max = np.nanmax(plls) plls = plls - w_global_max - - uvals=[] - + + uvals = [] + for i in np.arange(NDIMS): unique = np.arange(lls.shape[i]) uvals.append(unique) # we now have a list of unique values for each dimension - vectors=[] # this will contain the best values for 1d plots - wvectors=[] # holds same as above, but including spectral penalty factor from ASKAP obs - + vectors = [] # this will contain the best values for 1d plots + wvectors = ( + [] + ) # holds same as above, but including spectral penalty factor from ASKAP obs + # loop over the DIMS for i in np.arange(NDIMS): - + # does 1D values - vector=np.zeros([len(uvals[i])]) - wvector=np.zeros([len(uvals[i])]) + vector = np.zeros([len(uvals[i])]) + wvector = np.zeros([len(uvals[i])]) # selects for lls a subset corresponding only to that particular value of a variables for iv, ivv in enumerate(uvals[i]): - big_slice = [slice(None,None,None)]*NDIMS + big_slice = [slice(None, None, None)] * NDIMS # Construct the slice big_slice[i] = ivv - #set1=np.where(data[:,idim]==ivv) #selects for a set of values - #lls=data[set1,llindex] - lls=origlls[tuple(big_slice)].flatten() - + # set1=np.where(data[:,idim]==ivv) #selects for a set of values + # lls=data[set1,llindex] + lls = origlls[tuple(big_slice)].flatten() + # ignores all values of 0, which is what missing data is - ignore=np.where(lls == 0.)[0] - lls[ignore]=-99999 - + ignore = np.where(lls == 0.0)[0] + lls[ignore] = -99999 + # selects all fits that are close to the peak (i.e. percentage within 0.1%) try: - themax=np.nanmax(lls) + themax = np.nanmax(lls) except: # all nans, probability =0. Easy! - vector[iv]=0. - wvector[iv]=0. + vector[iv] = 0.0 + wvector[iv] = 0.0 continue - - OKlls=np.isfinite(lls) & (lls > themax-3) - vector[iv]=np.sum(10**lls[OKlls]) - + + OKlls = np.isfinite(lls) & (lls > themax - 3) + vector[iv] = np.sum(10 ** lls[OKlls]) + if plls is not None: - wlls=plls[tuple(big_slice)].flatten() - wthemax=np.nanmax(wlls) - OKwlls=np.isfinite(wlls) & (wlls > wthemax-3) - wvector[iv]=np.sum(10**wlls[OKwlls]) - - #import pdb; pdb.set_trace() + wlls = plls[tuple(big_slice)].flatten() + wthemax = np.nanmax(wlls) + OKwlls = np.isfinite(wlls) & (wlls > wthemax - 3) + wvector[iv] = np.sum(10 ** wlls[OKwlls]) + + # import pdb; pdb.set_trace() # Check - vector *= 1./np.sum(vector) - vector *= 1./np.abs(uvals[i][1]-uvals[i][0]) + vector *= 1.0 / np.sum(vector) + vector *= 1.0 / np.abs(uvals[i][1] - uvals[i][0]) vectors.append(vector) if plls is not None: - wvector *= 1./np.sum(wvector) - wvector *= 1./np.abs(uvals[i][1]-uvals[i][0]) - wvectors.append(wvector) - - + wvector *= 1.0 / np.sum(wvector) + wvector *= 1.0 / np.abs(uvals[i][1] - uvals[i][0]) + wvectors.append(wvector) + # now makes correction lls += global_max if plls is not None: plls += w_global_max - + # Pickle? if pklfile is not None: - with open(pklfile, 'wb') as output: + with open(pklfile, "wb") as output: pickle.dump(uvals, output, pickle.HIGHEST_PROTOCOL) pickle.dump(vectors, output, pickle.HIGHEST_PROTOCOL) pickle.dump(wvectors, output, pickle.HIGHEST_PROTOCOL) - + # result is just the total probability, normalised to unity, when summed over the parameter space # technically needs to be divided by the x-increment in bins. - return uvals,vectors,wvectors + return uvals, vectors, wvectors -def get_2D_bayesian_data(lls:np.ndarray, - plls:np.ndarray=None, - pklfile=None): +def get_2D_bayesian_data(lls: np.ndarray, plls: np.ndarray = None, pklfile=None): """ Method to perform simple Bayesian analysis on the Log-likelihood cube @@ -414,105 +450,104 @@ def get_2D_bayesian_data(lls:np.ndarray, ijs, arrays, and warrays have Nitems = Nparams*(Nparams-1)/2 """ - NDIMS= len(lls.shape) - + NDIMS = len(lls.shape) + # multiplies all log-likelihoods by the maximum value global_max = np.nanmax(lls) lls -= global_max - - #eventually remove this line + + # eventually remove this line if plls is None: plls = lls - + if plls is not None: w_global_max = np.nanmax(plls) plls = plls - w_global_max - - origlls=lls - uvals=[] - + + origlls = lls + uvals = [] + for i in np.arange(NDIMS): unique = np.arange(lls.shape[i]) uvals.append(unique) # we now have a list of unique values for each dimension - arrays=[] # this will contain the best values for 1d plots - warrays=[] # holds same as above, but including spectral penalty factor from ASKAP obs - ijs=[] - + arrays = [] # this will contain the best values for 1d plots + warrays = ( + [] + ) # holds same as above, but including spectral penalty factor from ASKAP obs + ijs = [] + # loop over the first dimensional combination for i in np.arange(NDIMS): - + # loops over the second dimension - for j in (np.arange(NDIMS-i-1)+i+1): - + for j in np.arange(NDIMS - i - 1) + i + 1: + # does 1D values - array=np.zeros([len(uvals[i]),len(uvals[j])]) - warray=np.zeros([len(uvals[i]),len(uvals[j])]) - + array = np.zeros([len(uvals[i]), len(uvals[j])]) + warray = np.zeros([len(uvals[i]), len(uvals[j])]) + # selects for lls a subset corresponding only to that particular value of a variables for iv, ivv in enumerate(uvals[i]): - big_slice = [slice(None,None,None)]*NDIMS + big_slice = [slice(None, None, None)] * NDIMS # Construct the slice big_slice[i] = ivv - + for jv, jvv in enumerate(uvals[j]): # Construct the slice big_slice[j] = jvv - - - #lls=data[set1,llindex] - lls=origlls[tuple(big_slice)].flatten() - + + # lls=data[set1,llindex] + lls = origlls[tuple(big_slice)].flatten() + # ignores all values of 0, which is what missing data is - ignore=np.where(lls == 0.)[0] - lls[ignore]=-99999 - + ignore = np.where(lls == 0.0)[0] + lls[ignore] = -99999 + try: - themax=np.nanmax(lls) + themax = np.nanmax(lls) except: # all nans, probability =0. Easy! - arrays[iv,jv]=0. - warrays[iv,jv]=0. + arrays[iv, jv] = 0.0 + warrays[iv, jv] = 0.0 continue - - OKlls=np.isfinite(lls) & (lls > themax-3) - array[iv,jv]=np.sum(10**lls[OKlls]) - + + OKlls = np.isfinite(lls) & (lls > themax - 3) + array[iv, jv] = np.sum(10 ** lls[OKlls]) + if plls is not None: - wlls=plls[tuple(big_slice)].flatten() - wthemax=np.nanmax(wlls) - OKwlls=np.isfinite(wlls) & (wlls > wthemax-3) - warray[iv,jv]=np.sum(10**wlls[OKwlls]) - - - #normalisation over the parameter space to unity - array *= 1./np.sum(array) + wlls = plls[tuple(big_slice)].flatten() + wthemax = np.nanmax(wlls) + OKwlls = np.isfinite(wlls) & (wlls > wthemax - 3) + warray[iv, jv] = np.sum(10 ** wlls[OKwlls]) + + # normalisation over the parameter space to unity + array *= 1.0 / np.sum(array) arrays.append(array) if plls is not None: - warray *= 1./np.sum(warray) + warray *= 1.0 / np.sum(warray) warrays.append(warray) - - ijs.append([i,j]) - + + ijs.append([i, j]) + lls += global_max if plls is not None: plls += w_global_max - + # Pickle? if pklfile is not None: - with open(pklfile, 'wb') as output: + with open(pklfile, "wb") as output: pickle.dump(uvals, output, pickle.HIGHEST_PROTOCOL) pickle.dump(vectors, output, pickle.HIGHEST_PROTOCOL) pickle.dump(wvectors, output, pickle.HIGHEST_PROTOCOL) - + # result is just the total probability, normalised to unity, when summed over the parameter space # technically needs to be divided by the x-increment in bins. - return uvals,ijs,arrays,warrays + return uvals, ijs, arrays, warrays -def get_maxl_data(lls:np.ndarray, - plls:np.ndarray=None, - pklfile=None): + +def get_maxl_data(lls: np.ndarray, plls: np.ndarray = None, pklfile=None): """ Method to perform simple Bayesian analysis on the Log-likelihood cube @@ -527,99 +562,95 @@ def get_maxl_data(lls:np.ndarray, lists of np.ndarray's of LL analysis One item per parameter in the cube """ - NDIMS= len(lls.shape) - + NDIMS = len(lls.shape) + # multiplies all log-likelihoods by the maximum value # ensures no floating point problems global_max = np.nanmax(lls) lls -= global_max - - #eventually remove this line + + # eventually remove this line if plls is None: plls = lls - - origlls=lls - + + origlls = lls + if plls is not None: w_global_max = np.nanmax(plls) plls = plls - w_global_max - - - - - uvals=[] - + + uvals = [] + for i in np.arange(NDIMS): unique = np.arange(lls.shape[i]) uvals.append(unique) # we now have a list of unique values for each dimension - vectors=[] # this will contain the best values for 1d plots - wvectors=[] # holds same as above, but including spectral penalty factor from ASKAP obs - + vectors = [] # this will contain the best values for 1d plots + wvectors = ( + [] + ) # holds same as above, but including spectral penalty factor from ASKAP obs + # loop over the DIMS for i in np.arange(NDIMS): - + # does 1D values - vector=np.zeros([len(uvals[i])]) - wvector=np.zeros([len(uvals[i])]) + vector = np.zeros([len(uvals[i])]) + wvector = np.zeros([len(uvals[i])]) # selects for lls a subset corresponding only to that particular value of a variables for iv, ivv in enumerate(uvals[i]): - big_slice = [slice(None,None,None)]*NDIMS + big_slice = [slice(None, None, None)] * NDIMS # Construct the slice big_slice[i] = ivv - #set1=np.where(data[:,idim]==ivv) #selects for a set of values - #lls=data[set1,llindex] - lls=origlls[tuple(big_slice)].flatten() - + # set1=np.where(data[:,idim]==ivv) #selects for a set of values + # lls=data[set1,llindex] + lls = origlls[tuple(big_slice)].flatten() + # ignores all values of 0, which is what missing data is - ignore=np.where(lls == 0.)[0] - lls[ignore]=-99999 - + ignore = np.where(lls == 0.0)[0] + lls[ignore] = -99999 + # selects all fits that are close to the peak (i.e. percentage within 0.1%) try: - themax=np.nanmax(lls) + themax = np.nanmax(lls) except: # all nans, probability =0. Easy! - vector[iv]=0. - wvector[iv]=0. + vector[iv] = 0.0 + wvector[iv] = 0.0 continue - - vector[iv]=themax - + + vector[iv] = themax + if plls is not None: - wlls=plls[tuple(big_slice)].flatten() - wthemax=np.nanmax(wlls) - wvector[iv]=wthemax - - #import pdb; pdb.set_trace() + wlls = plls[tuple(big_slice)].flatten() + wthemax = np.nanmax(wlls) + wvector[iv] = wthemax + + # import pdb; pdb.set_trace() # Check vectors.append(vector) if plls is not None: - wvectors.append(wvector) - - + wvectors.append(wvector) + # now makes correction lls += global_max if plls is not None: plls += w_global_max - + # Pickle? if pklfile is not None: - with open(pklfile, 'wb') as output: + with open(pklfile, "wb") as output: pickle.dump(uvals, output, pickle.HIGHEST_PROTOCOL) pickle.dump(vectors, output, pickle.HIGHEST_PROTOCOL) pickle.dump(wvectors, output, pickle.HIGHEST_PROTOCOL) - + # result is just the total probability, normalised to unit, when summed over the parameter space # technically needs to be divided by the x-increment in bins. - return uvals,vectors,wvectors + return uvals, vectors, wvectors -def get_2D_maxl_data(lls:np.ndarray, - plls:np.ndarray=None, - pklfile=None): +def get_2D_maxl_data(lls: np.ndarray, plls: np.ndarray = None, pklfile=None): """ Method to perform simple Bayesian analysis on the Log-likelihood cube @@ -638,98 +669,99 @@ def get_2D_maxl_data(lls:np.ndarray, ijs, arrays, and warrays have Nitems = Nparams*(Nparams-1)/2 """ - NDIMS= len(lls.shape) - + NDIMS = len(lls.shape) + # multiplies all log-likelihoods by the maximum value global_max = np.nanmax(lls) lls -= global_max - - #eventually remove this line + + # eventually remove this line if plls is None: plls = lls - + if plls is not None: w_global_max = np.nanmax(plls) plls = plls - w_global_max - - origlls=lls - uvals=[] - + + origlls = lls + uvals = [] + for i in np.arange(NDIMS): unique = np.arange(lls.shape[i]) uvals.append(unique) # we now have a list of unique values for each dimension - arrays=[] # this will contain the best values for 1d plots - warrays=[] # holds same as above, but including spectral penalty factor from ASKAP obs - ijs=[] - + arrays = [] # this will contain the best values for 1d plots + warrays = ( + [] + ) # holds same as above, but including spectral penalty factor from ASKAP obs + ijs = [] + # loop over the first dimensional combination for i in np.arange(NDIMS): - + # loops over the second dimension - for j in (np.arange(NDIMS-i-1)+i+1): - + for j in np.arange(NDIMS - i - 1) + i + 1: + # does 1D values - array=np.zeros([len(uvals[i]),len(uvals[j])]) - warray=np.zeros([len(uvals[i]),len(uvals[j])]) - + array = np.zeros([len(uvals[i]), len(uvals[j])]) + warray = np.zeros([len(uvals[i]), len(uvals[j])]) + # selects for lls a subset corresponding only to that particular value of a variables for iv, ivv in enumerate(uvals[i]): - big_slice = [slice(None,None,None)]*NDIMS + big_slice = [slice(None, None, None)] * NDIMS # Construct the slice big_slice[i] = ivv - + for jv, jvv in enumerate(uvals[j]): # Construct the slice big_slice[j] = jvv - lls=origlls[tuple(big_slice)].flatten() - + lls = origlls[tuple(big_slice)].flatten() + # ignores all values of 0, which is what missing data is - ignore=np.where(lls == 0.)[0] - lls[ignore]=-99999 - + ignore = np.where(lls == 0.0)[0] + lls[ignore] = -99999 + try: - themax=np.nanmax(lls) + themax = np.nanmax(lls) except: # all nans, probability =0. Easy! - arrays[iv,jv]=0. - warrays[iv,jv]=0. + arrays[iv, jv] = 0.0 + warrays[iv, jv] = 0.0 continue - - array[iv,jv]=themax - + + array[iv, jv] = themax + if plls is not None: - wlls=plls[tuple(big_slice)].flatten() - wthemax=np.nanmax(wlls) - warray[iv,jv]=wthemax - - - #normalisation over the parameter space to unity + wlls = plls[tuple(big_slice)].flatten() + wthemax = np.nanmax(wlls) + warray[iv, jv] = wthemax + + # normalisation over the parameter space to unity arrays.append(array) if plls is not None: - warray *= 1./np.sum(warray) + warray *= 1.0 / np.sum(warray) warrays.append(warray) - - ijs.append([i,j]) - + + ijs.append([i, j]) + lls += global_max if plls is not None: plls += w_global_max - + # Pickle? if pklfile is not None: - with open(pklfile, 'wb') as output: + with open(pklfile, "wb") as output: pickle.dump(uvals, output, pickle.HIGHEST_PROTOCOL) pickle.dump(vectors, output, pickle.HIGHEST_PROTOCOL) pickle.dump(wvectors, output, pickle.HIGHEST_PROTOCOL) - + # result is just the total probability, normalised to unity, when summed over the parameter space # technically needs to be divided by the x-increment in bins. - return uvals,ijs,arrays,warrays + return uvals, ijs, arrays, warrays -def interpolate_points(oldx,oldy,logspline=False, kind='cubic'): +def interpolate_points(oldx, oldy, logspline=False, kind="cubic"): """ performs simle spline interpolation of the data args: @@ -740,31 +772,32 @@ def interpolate_points(oldx,oldy,logspline=False, kind='cubic'): returns: x,y: interpolated points """ - + #### does unweighted plotting #### - x=np.linspace(oldx[0],oldx[-1],400) - + x = np.linspace(oldx[0], oldx[-1], 400) + if logspline: # does interpolation in log-space - f=scipy.interpolate.interp1d(oldx,np.log(oldy), kind=kind) - y=np.exp(f(x)) + f = scipy.interpolate.interp1d(oldx, np.log(oldy), kind=kind) + y = np.exp(f(x)) else: - f=scipy.interpolate.interp1d(oldx,oldy, kind=kind) - y=f(x) + f = scipy.interpolate.interp1d(oldx, oldy, kind=kind) + y = f(x) # check to ensure the splines have not done anything too dumb - - f2=scipy.interpolate.interp1d(oldx,np.log(oldy), kind=kind) - y2=np.exp(f2(x)) - + + f2 = scipy.interpolate.interp1d(oldx, np.log(oldy), kind=kind) + y2 = np.exp(f2(x)) + # replaces linear interpolation in small-value regime where splines can be dumb - if np.min(y2) < 1.e-2: + if np.min(y2) < 1.0e-2: very_small = np.where(y2 < 1e-2)[0] - y[very_small]=y2[very_small] - - y[np.where(y < 0.)]=0. - return x,y + y[very_small] = y2[very_small] + + y[np.where(y < 0.0)] = 0.0 + return x, y + -def extract_limits(x,y,p,method=1): +def extract_limits(x, y, p, method=1): """ args: x (np.ndarray): xvalues of data (independent variable on which we place limits) @@ -775,44 +808,59 @@ def extract_limits(x,y,p,method=1): returns: v0,v1: lower and upper bounds of range """ - + # sets intervals according to highest likelihood - if method==1: + if method == 1: # this sorts from lowest to highest - sy=np.sort(y) + sy = np.sort(y) # highest to lowest - sy=sy[::-1] + sy = sy[::-1] # now 0 to 1 - csy=np.cumsum(sy) + csy = np.cumsum(sy) csy /= csy[-1] - + # this is the likelihood we cut on - cut=np.where(csy < 1.-2.*p)[0] # allowed values in interval - cut=cut[-1] # last allowed value - cut=sy[cut] - OK=np.where(y > cut)[0] - ik1=OK[0] - ik2=OK[-1] - v0=x[ik1] - v1=x[ik2] - elif method==2: - cy=np.cumsum(y) - cy /= cy[-1] # ignores normalisation in dx direction - + cut = np.where(csy < 1.0 - 2.0 * p)[0] # allowed values in interval + cut = cut[-1] # last allowed value + cut = sy[cut] + OK = np.where(y > cut)[0] + ik1 = OK[0] + ik2 = OK[-1] + v0 = x[ik1] + v1 = x[ik2] + elif method == 2: + cy = np.cumsum(y) + cy /= cy[-1] # ignores normalisation in dx direction + # gets lower value - inside=np.where(cy > p)[0] - ik1=inside[0] - v0=x[ik1] + inside = np.where(cy > p)[0] + ik1 = inside[0] + v0 = x[ik1] # gets upper value - inside=np.where(cy > 1.-p)[0] - ik2=inside[0] - v1=x[ik2] - return v0,v1,ik1,ik2 - -def do_single_plots(uvals,vectors,wvectors,names,tag=None, fig_exten='.png', - dolevels=False,log=True,outdir='SingleFigs/', - vparams_dict=None, prefix='',truth=None,latexnames=None, - units=None,logspline=True, others=None): + inside = np.where(cy > 1.0 - p)[0] + ik2 = inside[0] + v1 = x[ik2] + return v0, v1, ik1, ik2 + + +def do_single_plots( + uvals, + vectors, + wvectors, + names, + tag=None, + fig_exten=".png", + dolevels=False, + log=True, + outdir="SingleFigs/", + vparams_dict=None, + prefix="", + truth=None, + latexnames=None, + units=None, + logspline=True, + others=None, +): """ Generate a series of 1D plots of the cube parameters Args: @@ -839,233 +887,362 @@ def do_single_plots(uvals,vectors,wvectors,names,tag=None, fig_exten='.png', others(list of arrays): list of other plots to add to data """ - + if tag is not None: - outdir=tag+outdir + outdir = tag + outdir if not os.path.isdir(outdir): - os.makedirs(outdir) - + os.makedirs(outdir) + if log: - logfile=outdir+'limits.dat' - logfile=open(logfile,'w') - + logfile = outdir + "limits.dat" + logfile = open(logfile, "w") + if dolevels: - results=np.zeros([len(uvals),9]) # holds mean and error info for each parameter - prior_results=np.zeros([len(uvals),9]) # does the same with alpha priors - - for i,vals in enumerate(uvals): + results = np.zeros( + [len(uvals), 9] + ) # holds mean and error info for each parameter + prior_results = np.zeros([len(uvals), 9]) # does the same with alpha priors + + for i, vals in enumerate(uvals): + + kind = None + if len(vals) == 1: continue if len(vals) < 4: - kind = 'linear' + kind = "linear" else: - kind = 'cubic' + kind = "cubic" # does the for alpha plt.figure() - lw=3 - + lw = 3 # Convert vals? if vparams_dict is not None: # Check - assert vparams_dict[names[i]]['n'] == len(vals) - vals = np.linspace(vparams_dict[names[i]]['min'], - vparams_dict[names[i]]['max'], - len(vals)) - + assert vparams_dict[names[i]]["n"] == len(vals) + vals = np.linspace( + vparams_dict[names[i]]["min"], vparams_dict[names[i]]["max"], len(vals) + ) + # get raw ylimits # removes zeroes, could lead to strange behaviour in theory - ymax=np.max(vectors[i]) - temp=np.where((vectors[i] > 0.) & (np.isfinite(vectors[i])) ) - + ymax = np.max(vectors[i]) + temp = np.where((vectors[i] > 0.0) & (np.isfinite(vectors[i]))) + # set to integers and get range - ymax=math.ceil(ymax) - ymin=0. - - x,y=interpolate_points(vals[temp],vectors[i][temp],logspline) - - norm=np.sum(y)*(x[1]-x[0]) # integral y dx ~ sum y delta x - norm=np.abs(norm) + ymax = math.ceil(ymax) + ymin = 0.0 + + x, y = interpolate_points(vals[temp], vectors[i][temp], logspline, kind=kind) + + norm = np.sum(y) * (x[1] - x[0]) # integral y dx ~ sum y delta x + norm = np.abs(norm) y /= norm vectors[i][temp] /= norm - plt.plot(x,y,label='Uniform',color='blue',linewidth=lw,linestyle='-') - plt.plot(vals[temp],vectors[i][temp],color='blue',linestyle='',marker='s') - - + plt.plot(x, y, label="Uniform", color="blue", linewidth=lw, linestyle="-") + plt.plot(vals[temp], vectors[i][temp], color="blue", linestyle="", marker="s") + # weighted plotting if wvectors is not None: - wx,wy=interpolate_points(vals[temp],wvectors[i][temp],logspline) - wnorm=np.sum(wy)*(x[1]-x[0]) + wx, wy = interpolate_points( + vals[temp], wvectors[i][temp], logspline, kind=kind + ) + wnorm = np.sum(wy) * (x[1] - x[0]) wnorm = np.abs(wnorm) - + wvectors[i][temp] /= wnorm wy /= wnorm - plt.plot(x,wy,label='Gauss',color='orange',linewidth=lw,linestyle='--') - - ax=plt.gca() - ax.xaxis.set_ticks_position('both') - #ax.Xaxis.set_ticks_position('both') + plt.plot(x, wy, label="Gauss", color="orange", linewidth=lw, linestyle="--") + + ax = plt.gca() + ax.xaxis.set_ticks_position("both") + # ax.Xaxis.set_ticks_position('both') if wvectors is not None: - ymax=np.max([np.max(wy),np.max(y)]) + ymax = np.max([np.max(wy), np.max(y)]) else: - ymax=np.max(y) - - #ymax=(np.ceil(ymax*5.))/5. - - - if dolevels==True:# and i != 1: - limvals=np.array([0.00135,0.0228,0.05,0.15866]) - labels=['99.7%','95%','90%','68%'] - styles=['--',':','-.','-'] - upper=np.max(vectors[i]) - - besty=np.max(y) - imax=np.argmax(y) - xmax=x[imax] - results[i,0]=xmax - string=names[i]+" & {0:4.2f}".format(xmax) - for iav,av in enumerate(limvals): + ymax = np.max(y) + + # ymax=(np.ceil(ymax*5.))/5. + + if dolevels == True: # and i != 1: + limvals = np.array([0.00135, 0.0228, 0.05, 0.15866]) + labels = ["99.7%", "95%", "90%", "68%"] + styles = ["--", ":", "-.", "-"] + upper = np.max(vectors[i]) + + besty = np.max(y) + imax = np.argmax(y) + xmax = x[imax] + results[i, 0] = xmax + string = names[i] + " & {0:4.2f}".format(xmax) + for iav, av in enumerate(limvals): # need to integrate from min to some point # gets cumulative distribution # sets intervals according to highest likelihood - - v0,v1,ik1,ik2=extract_limits(x,y,av,method=1) - + + v0, v1, ik1, ik2 = extract_limits(x, y, av, method=1) + string += " & $_{" - string += "{0:4.2f}".format(v0-xmax) + string += "{0:4.2f}".format(v0 - xmax) string += "}^{+" - string += "{0:4.2f}".format(v1-xmax) + string += "{0:4.2f}".format(v1 - xmax) string += "}$ " - results[i,2*iav+1]=v0-xmax - results[i,2*iav+2]=v1-xmax - - hl=0.03 - doff=(x[-1]-x[0])/100. - ybar=(av+ymax)/2. - xbar=(v0+v1)/2. - + results[i, 2 * iav + 1] = v0 - xmax + results[i, 2 * iav + 2] = v1 - xmax + + hl = 0.03 + doff = (x[-1] - x[0]) / 100.0 + ybar = (av + ymax) / 2.0 + xbar = (v0 + v1) / 2.0 + # need to separate the plots if wvectors is not None: if ik1 != 0: - #if iav==3 and i==4: + # if iav==3 and i==4: # ybar -= 0.8 - plt.plot([x[ik1],x[ik1]],[ymax,y[ik1]],color='blue',linestyle=styles[iav],alpha=0.5) - if i==1: - t=plt.text(x[ik1]+doff*0.5,(ymax)+(-3.6+iav)*0.2*ymax,labels[iav],rotation=90,fontsize=12) - t.set_bbox(dict(facecolor='white', alpha=0.7, edgecolor='white',pad=-1)) - if ik2 != wy.size-1: - plt.plot([x[ik2],x[ik2]],[ymax,y[ik2]],color='blue',linestyle=styles[iav],alpha=0.5) + plt.plot( + [x[ik1], x[ik1]], + [ymax, y[ik1]], + color="blue", + linestyle=styles[iav], + alpha=0.5, + ) + if i == 1: + t = plt.text( + x[ik1] + doff * 0.5, + (ymax) + (-3.6 + iav) * 0.2 * ymax, + labels[iav], + rotation=90, + fontsize=12, + ) + t.set_bbox( + dict( + facecolor="white", + alpha=0.7, + edgecolor="white", + pad=-1, + ) + ) + if ik2 != wy.size - 1: + plt.plot( + [x[ik2], x[ik2]], + [ymax, y[ik2]], + color="blue", + linestyle=styles[iav], + alpha=0.5, + ) if i != 1: - t=plt.text(x[ik2]-doff*3,(ymax)+(-3.6+iav)*0.2*ymax,labels[iav],rotation=90,fontsize=12) - t.set_bbox(dict(facecolor='white', alpha=0.7, edgecolor='white',pad=-1)) + t = plt.text( + x[ik2] - doff * 3, + (ymax) + (-3.6 + iav) * 0.2 * ymax, + labels[iav], + rotation=90, + fontsize=12, + ) + t.set_bbox( + dict( + facecolor="white", + alpha=0.7, + edgecolor="white", + pad=-1, + ) + ) else: - plt.plot([x[ik1],x[ik1]],[0,y[ik1]],color='red',linestyle=styles[iav]) - plt.plot([x[ik2],x[ik2]],[0,y[ik2]],color='red',linestyle=styles[iav]) - Dx=x[-1]-x[0] - if Dx < 0.: - plt.text(x[ik1],y[ik1]+ymax*0.03,labels[iav],color='red',rotation=90) - plt.text(x[ik2]+0.02*Dx,y[ik2]+ymax*0.03,labels[iav],color='red',rotation=90) + plt.plot( + [x[ik1], x[ik1]], + [0, y[ik1]], + color="red", + linestyle=styles[iav], + ) + plt.plot( + [x[ik2], x[ik2]], + [0, y[ik2]], + color="red", + linestyle=styles[iav], + ) + Dx = x[-1] - x[0] + if Dx < 0.0: + plt.text( + x[ik1], + y[ik1] + ymax * 0.03, + labels[iav], + color="red", + rotation=90, + ) + plt.text( + x[ik2] + 0.02 * Dx, + y[ik2] + ymax * 0.03, + labels[iav], + color="red", + rotation=90, + ) else: - plt.text(x[ik1]-0.02*Dx,y[ik1]+ymax*0.03,labels[iav],color='red',rotation=90) - plt.text(x[ik2],y[ik2]+ymax*0.03,labels[iav],color='red',rotation=90) - #print("For parameter ",i," CI ",iav, " is ",x[ik1]," to ",x[ik2]) + plt.text( + x[ik1] - 0.02 * Dx, + y[ik1] + ymax * 0.03, + labels[iav], + color="red", + rotation=90, + ) + plt.text( + x[ik2], + y[ik2] + ymax * 0.03, + labels[iav], + color="red", + rotation=90, + ) + # print("For parameter ",i," CI ",iav, " is ",x[ik1]," to ",x[ik2]) string += " & " - - #could just ignore the weightings + + # could just ignore the weightings if wvectors is not None: - plt.plot(vals[temp],wvectors[i][temp],color='orange',linestyle='',marker='o') - if dolevels==True: - limvals=np.array([0.0015,0.025,0.05,0.16]) - labels=['99.7%','95%','90%','68%'] - styles=['--',':','-.','-'] - upper=np.max(wvectors[i]) - - besty=np.max(wy) - imax=np.argmax(wy) - xmax=x[imax] - prior_results[i,0]=xmax - string+=" {0:4.2f}".format(xmax) - for iav,av in enumerate(limvals): - + plt.plot( + vals[temp], wvectors[i][temp], color="orange", linestyle="", marker="o" + ) + if dolevels == True: + limvals = np.array([0.0015, 0.025, 0.05, 0.16]) + labels = ["99.7%", "95%", "90%", "68%"] + styles = ["--", ":", "-.", "-"] + upper = np.max(wvectors[i]) + + besty = np.max(wy) + imax = np.argmax(wy) + xmax = x[imax] + prior_results[i, 0] = xmax + string += " {0:4.2f}".format(xmax) + for iav, av in enumerate(limvals): + # sets intervals according to highest likelihood - v0,v1,ik1,ik2=extract_limits(x,wy,av,method=1) - + v0, v1, ik1, ik2 = extract_limits(x, wy, av, method=1) + string += " & $_{" - string += "{0:4.2f}".format(v0-xmax) + string += "{0:4.2f}".format(v0 - xmax) string += "}^{+" - string += "{0:4.2f}".format(v1-xmax) + string += "{0:4.2f}".format(v1 - xmax) string += "}$ " - prior_results[i,2*iav+1]=v0-xmax - prior_results[i,2*iav+2]=v1-xmax - + prior_results[i, 2 * iav + 1] = v0 - xmax + prior_results[i, 2 * iav + 2] = v1 - xmax + # version 2 - hl=0.03 - - doff=(x[-1]-x[0])/100. - if i==1: - doff=0.001 - ybar=(av+ymin)/2. - xbar=(v0+v1)/2. + hl = 0.03 + + doff = (x[-1] - x[0]) / 100.0 + if i == 1: + doff = 0.001 + ybar = (av + ymin) / 2.0 + xbar = (v0 + v1) / 2.0 if ik1 != 0: - plt.plot([x[ik1],x[ik1]],[ymin,wy[ik1]],color='orange',linestyle=styles[iav]) - if i ==1: - t=plt.text(x[ik1]+doff*0.5,wy[ik1]/2.2,labels[iav],rotation=90,fontsize=12) - t.set_bbox(dict(facecolor='white', alpha=0.7, edgecolor='white',pad=-1)) - - if ik2 != wy.size-1: - - plt.plot([x[ik2],x[ik2]],[ymin,wy[ik2]],color='orange',linestyle=styles[iav]) + plt.plot( + [x[ik1], x[ik1]], + [ymin, wy[ik1]], + color="orange", + linestyle=styles[iav], + ) + if i == 1: + t = plt.text( + x[ik1] + doff * 0.5, + wy[ik1] / 2.2, + labels[iav], + rotation=90, + fontsize=12, + ) + t.set_bbox( + dict( + facecolor="white", + alpha=0.7, + edgecolor="white", + pad=-1, + ) + ) + + if ik2 != wy.size - 1: + + plt.plot( + [x[ik2], x[ik2]], + [ymin, wy[ik2]], + color="orange", + linestyle=styles[iav], + ) if i != 1: - t=plt.text(x[ik2]-doff*3,wy[ik2]/2.2,labels[iav],rotation=90,fontsize=12) - t.set_bbox(dict(facecolor='white', alpha=0.7, edgecolor='white',pad=-1)) - other_styles=[":","--","-."] + t = plt.text( + x[ik2] - doff * 3, + wy[ik2] / 2.2, + labels[iav], + rotation=90, + fontsize=12, + ) + t.set_bbox( + dict( + facecolor="white", + alpha=0.7, + edgecolor="white", + pad=-1, + ) + ) + other_styles = [":", "--", "-."] # plot any other plots if others is not None: if others[i] is not None: - for io,data in enumerate(others[i]): - x,y=interpolate_points(vals,data,logspline) - norm=np.sum(y)*(x[1]-x[0]) # integral y dx ~ sum y delta x - norm=np.abs(norm) + for io, data in enumerate(others[i]): + x, y = interpolate_points(vals, data, logspline, kind=kind) + norm = np.sum(y) * (x[1] - x[0]) # integral y dx ~ sum y delta x + norm = np.abs(norm) y /= norm - plt.plot(x,y,color='grey',linewidth=1,linestyle=other_styles[io % 3]) + plt.plot( + x, y, color="grey", linewidth=1, linestyle=other_styles[io % 3] + ) if dolevels: string += "\\\\" if log: - logfile.write(string+'\n') + logfile.write(string + "\n") else: print(string) - #plt.ylim(0.,ymax) + # plt.ylim(0.,ymax) plt.gca().set_ylim(bottom=0) if truth is not None: - plt.plot([truth[i],truth[i]],plt.gca().get_ylim(),color='black',linestyle=':') - Dx=x[-1]-x[0] - plt.text(truth[i]+0.01*Dx,ymax*0.4,'simulated truth',rotation=90) - + plt.plot( + [truth[i], truth[i]], plt.gca().get_ylim(), color="black", linestyle=":" + ) + Dx = x[-1] - x[0] + plt.text(truth[i] + 0.01 * Dx, ymax * 0.4, "simulated truth", rotation=90) + if latexnames is not None: if units is not None: - plt.xlabel(latexnames[i]+" "+units[i]) + plt.xlabel(latexnames[i] + " " + units[i]) else: plt.xlabel(latexnames[i]) - plt.ylabel('$p($'+latexnames[i]+'$)$') + plt.ylabel("$p($" + latexnames[i] + "$)$") else: plt.xlabel(names[i]) - plt.ylabel('p('+names[i]+')') - if i==4 and wvectors is not None: - plt.legend(loc='upper left',title='Prior on $\\alpha$') - + plt.ylabel("p(" + names[i] + ")") + if i == 4 and wvectors is not None: + plt.legend(loc="upper left", title="Prior on $\\alpha$") + plt.tight_layout() - plt.savefig(os.path.join(outdir, prefix+names[i]+fig_exten), dpi=200) + plt.savefig(os.path.join(outdir, prefix + names[i] + fig_exten), dpi=200) plt.close() if log: logfile.close() if dolevels: - return results,prior_results + return results, prior_results else: return -def make_1d_plots_by_contribution(data,contributions,labels,prefix="", - fig_exten='.png',log=False,splines=True,latexnames=None,units=None, - linestyles=None,colors=None): + +def make_1d_plots_by_contribution( + data, + contributions, + labels, + prefix="", + fig_exten=".png", + log=False, + splines=True, + latexnames=None, + units=None, + linestyles=None, + colors=None, +): """ contributions: list of vectors giving various likelihood terms args: @@ -1075,79 +1252,116 @@ def make_1d_plots_by_contribution(data,contributions,labels,prefix="", units: appends units to x axis but not p(X) """ ######################### 1D plots, split by terms ################ - all_uvals=[] - all_vectors=[] - all_wvectors=[] - combined=data["pzDM"]+data["pDM"] - + all_uvals = [] + all_vectors = [] + all_wvectors = [] + combined = data["pzDM"] + data["pDM"] + # gets 1D Bayesian curves for each contribution for datatype in contributions: - uvals,vectors,wvectors=get_bayesian_data(datatype) + uvals, vectors, wvectors = get_bayesian_data(datatype) all_uvals.append(uvals) all_vectors.append(vectors) all_wvectors.append(wvectors) - params=data["params"] - + params = data["params"] + # gets unique values for each axis - param_vals=[] - param_list=[data["lEmax"],data["H0"],data["alpha"],data["gamma"],data["sfr_n"],data["lmean"],data["lsigma"]] - xlatexnames=['\\log_{10} E_{\\rm max} {\\rm [erg]}','H_0 {\\rm [km\,s^{-1}\,Mpc^{-1}]}','\\alpha','\\gamma','n_{\\rm sfr}','\\mu_{\\rm host} {\\rm [pc\,cm^{-3}]}','\\sigma_{\\rm host}'] - ylatexnames=['\\log_{10} E_{\\rm max}','H_0','\\alpha','\\gamma','n_{\\rm sfr}','\\mu_{\\rm host}','\\sigma_{\\rm host}'] - + param_vals = [] + param_list = [ + data["lEmax"], + data["H0"], + data["alpha"], + data["gamma"], + data["sfr_n"], + data["lmean"], + data["lsigma"], + ] + xlatexnames = [ + "\\log_{10} E_{\\rm max} {\\rm [erg]}", + "H_0 {\\rm [km\,s^{-1}\,Mpc^{-1}]}", + "\\alpha", + "\\gamma", + "n_{\\rm sfr}", + "\\mu_{\\rm host} {\\rm [pc\,cm^{-3}]}", + "\\sigma_{\\rm host}", + ] + ylatexnames = [ + "\\log_{10} E_{\\rm max}", + "H_0", + "\\alpha", + "\\gamma", + "n_{\\rm sfr}", + "\\mu_{\\rm host}", + "\\sigma_{\\rm host}", + ] + for col in param_list: - unique=np.unique(col) + unique = np.unique(col) param_vals.append(unique) # assigns different plotting styles to help distinguish curves if linestyles is None: - linestyles=['-','--','-.',':','-','--','-.',':','-','--','-.',':'] + linestyles = ["-", "--", "-.", ":", "-", "--", "-.", ":", "-", "--", "-.", ":"] if colors is None: - colors=plt.rcParams['axes.prop_cycle'].by_key()['color'] + colors = plt.rcParams["axes.prop_cycle"].by_key()["color"] for which in np.arange(len(param_list)): plt.figure() - plt.xlabel('$'+xlatexnames[which]+'$') - plt.ylabel('$p('+ylatexnames[which]+')$') - xvals=param_vals[which] - - for idata,vectors in enumerate(all_vectors): + plt.xlabel("$" + xlatexnames[which] + "$") + plt.ylabel("$p(" + ylatexnames[which] + ")$") + xvals = param_vals[which] + + for idata, vectors in enumerate(all_vectors): if splines: - xdata=np.linspace(xvals[0],xvals[-1],100) - f=scipy.interpolate.interp1d(xvals,np.log(vectors[which]), - kind='cubic') - ydata=np.exp(f(xdata)) - plt.plot(xdata,ydata,label=labels[idata], - linestyle=linestyles[idata], color=colors[idata]) - plt.scatter(xvals,vectors[which],color=plt.gca().lines[-1].get_color()) + xdata = np.linspace(xvals[0], xvals[-1], 100) + f = scipy.interpolate.interp1d( + xvals, np.log(vectors[which]), kind="cubic" + ) + ydata = np.exp(f(xdata)) + plt.plot( + xdata, + ydata, + label=labels[idata], + linestyle=linestyles[idata], + color=colors[idata], + ) + plt.scatter( + xvals, vectors[which], color=plt.gca().lines[-1].get_color() + ) else: - ydata=vectors[which] - xdata=xvals - #print(labels[idata]," has values ",vector) - plt.plot(xdata,ydata,label=labels[idata],linestyle=linestyles[idata], - color=colors[idata]) - + ydata = vectors[which] + xdata = xvals + # print(labels[idata]," has values ",vector) + plt.plot( + xdata, + ydata, + label=labels[idata], + linestyle=linestyles[idata], + color=colors[idata], + ) + if log: - plt.yscale('log') - #plt.ylim(np.max(vector)*1e-3,np.max(vector)) #improve this + plt.yscale("log") + # plt.ylim(np.max(vector)*1e-3,np.max(vector)) #improve this plt.legend() - plt.savefig(prefix+params[which]+fig_exten) + plt.savefig(prefix + params[which] + fig_exten) plt.close() - -def gen_vparams(indices:tuple, vparam_dict:dict): + + +def gen_vparams(indices: tuple, vparam_dict: dict): new_dict = {} for ss, key in enumerate(vparam_dict.keys()): - if vparam_dict[key]['n'] <= 0: + if vparam_dict[key]["n"] <= 0: continue - # - vals = np.linspace(vparam_dict[key]['min'], - vparam_dict[key]['max'], - vparam_dict[key]['n']) - # + # + vals = np.linspace( + vparam_dict[key]["min"], vparam_dict[key]["max"], vparam_dict[key]["n"] + ) + # new_dict[key] = vals[indices[ss]] # Return return new_dict - -def make_2d_plot(array,xlabel,ylabel,xvals,yvals,savename=None,norm=None): +def make_2d_plot(array, xlabel, ylabel, xvals, yvals, savename=None, norm=None): """ Makes 2D plot given an array of probabilities @@ -1164,40 +1378,45 @@ def make_2d_plot(array,xlabel,ylabel,xvals,yvals,savename=None,norm=None): plt.figure() plt.xlabel(xlabel) plt.ylabel(ylabel) - + # calculates increment in values - Dx=xvals[-1]-xvals[0] - Dy=yvals[-1]-yvals[0] - - nx=xvals.size - ny=yvals.size - - dx=np.abs(Dx/nx) - dy=np.abs(Dy/ny) - - aspect=np.abs(dx/dy * nx/ny) - + Dx = xvals[-1] - xvals[0] + Dy = yvals[-1] - yvals[0] + + nx = xvals.size + ny = yvals.size + + dx = np.abs(Dx / nx) + dy = np.abs(Dy / ny) + + aspect = np.abs(dx / dy * nx / ny) + # the dx/2 etc is so that parameter values line up with the centres of each pixel - extent=[np.min(xvals)-dx/2.,np.max(xvals)+dx/2.,np.min(yvals)-dy/2.,np.max(yvals)+dy/2.] - - if norm==0: - array=array/np.max(array,axis=0) - clabel='$p($'+ylabel+'|'+xlabel+'$)$' - elif norm==1: - array=array.T/np.max(array,axis=1) - array=array.T - clabel='$p($'+xlabel+'|'+ylabel+'$)$' + extent = [ + np.min(xvals) - dx / 2.0, + np.max(xvals) + dx / 2.0, + np.min(yvals) - dy / 2.0, + np.max(yvals) + dy / 2.0, + ] + + if norm == 0: + array = array / np.max(array, axis=0) + clabel = "$p($" + ylabel + "|" + xlabel + "$)$" + elif norm == 1: + array = array.T / np.max(array, axis=1) + array = array.T + clabel = "$p($" + xlabel + "|" + ylabel + "$)$" else: - clabel='$p($'+xlabel+','+ylabel+'$)$' - - plt.imshow(array.T,origin='lower',extent=extent,aspect=aspect) + clabel = "$p($" + xlabel + "," + ylabel + "$)$" + + plt.imshow(array.T, origin="lower", extent=extent, aspect=aspect) plt.xlabel(xlabel) plt.ylabel(ylabel) plt.xticks(rotation=90) - cbar=plt.colorbar() + cbar = plt.colorbar() cbar.set_label(clabel) if savename is None: - savename=xlabel+"_"+ylabel+".pdf" + savename = xlabel + "_" + ylabel + ".pdf" plt.tight_layout() plt.savefig(savename) plt.close() diff --git a/zdm/craco/mc.py b/zdm/craco/mc.py index 6f70060e..1d095c84 100644 --- a/zdm/craco/mc.py +++ b/zdm/craco/mc.py @@ -334,15 +334,37 @@ def do_basic_sample_plots(sample, opdir="Plots"): # JB +# generate( +# alpha_method=1, +# lum_func=2, +# Nsamples=1000, +# do_plots=True, +# outfile="MC_F/Surveys/F_vanilla_survey.dat", +# plotfile="MC_F/Plots/F_vanilla.pdf", +# savefile=None, +# # update_params={"F": 0.01}, +# ) + +# generate( +# alpha_method=1, +# lum_func=2, +# Nsamples=1000, +# do_plots=True, +# outfile="MC_F/Surveys/F_0.01_survey.dat", +# plotfile="MC_F/Plots/F_0.01.pdf", +# savefile=None, +# update_params={"F": 0.01}, +# ) + generate( alpha_method=1, lum_func=2, Nsamples=1000, do_plots=True, - outfile="MC_F/Surveys/F_vanilla_survey.dat", - plotfile="MC_F/Plots/F_vanilla.pdf", + outfile="MC_F/Surveys/F_0.32_survey.dat", + plotfile="MC_F/Plots/F_0.32.pdf", savefile=None, - # update_params={"F": 0.01}, + update_params={"F": 0.32}, ) # generate( @@ -350,10 +372,10 @@ def do_basic_sample_plots(sample, opdir="Plots"): # lum_func=2, # Nsamples=1000, # do_plots=True, -# outfile="MC_F/Surveys/F_0.01_survey.dat", -# plotfile="MC_F/Plots/F_0.01.pdf", +# outfile="MC_F/Surveys/F_0.7_survey.dat", +# plotfile="MC_F/Plots/F_0.7.pdf", # savefile=None, -# update_params={"F": 0.01}, +# update_params={"F": 0.7}, # ) # generate( @@ -361,10 +383,10 @@ def do_basic_sample_plots(sample, opdir="Plots"): # lum_func=2, # Nsamples=1000, # do_plots=True, -# outfile="MC_F/Surveys/F_0.9_survey.dat", -# plotfile="MC_F/Plots/F_0.9.pdf", +# outfile="MC_F/Surveys/F_0.01_dmhost_suppressed_survey.dat", +# plotfile="MC_F/Plots/F_0.01_dmhost_suppressed.pdf", # savefile=None, -# update_params={"F": 0.9}, +# update_params={"F": 0.01, "lmean": 1e-3, "lsigma": 0.1}, # ) # generate( @@ -372,52 +394,30 @@ def do_basic_sample_plots(sample, opdir="Plots"): # lum_func=2, # Nsamples=1000, # do_plots=True, -# outfile="MC_F/Surveys/F_0.7_survey.dat", -# plotfile="MC_F/Plots/F_0.7.pdf", +# outfile="MC_F/Surveys/F_0.9_dmhost_suppressed_survey.dat", +# plotfile="MC_F/Plots/F_0.9_dmhost_suppressed.pdf", # savefile=None, -# update_params={"F": 0.7}, +# update_params={"F": 0.9, "lmean": 1e-3, "lsigma": 0.1}, # ) -generate( - alpha_method=1, - lum_func=2, - Nsamples=1000, - do_plots=True, - outfile="MC_F/Surveys/F_0.01_dmhost_suppressed_survey.dat", - plotfile="MC_F/Plots/F_0.01_dmhost_suppressed.pdf", - savefile=None, - update_params={"F": 0.01, "lmean": 1e-3, "lsigma": 0.1}, -) - -generate( - alpha_method=1, - lum_func=2, - Nsamples=1000, - do_plots=True, - outfile="MC_F/Surveys/F_0.9_dmhost_suppressed_survey.dat", - plotfile="MC_F/Plots/F_0.9_dmhost_suppressed.pdf", - savefile=None, - update_params={"F": 0.9, "lmean": 1e-3, "lsigma": 0.1}, -) - -generate( - alpha_method=1, - lum_func=2, - Nsamples=1000, - do_plots=True, - outfile="MC_F/Surveys/F_0.7_dmhost_suppressed_survey.dat", - plotfile="MC_F/Plots/F_0.7_dmhost_suppressed.pdf", - savefile=None, - update_params={"F": 0.7, "lmean": 1e-3, "lsigma": 0.1}, -) +# generate( +# alpha_method=1, +# lum_func=2, +# Nsamples=1000, +# do_plots=True, +# outfile="MC_F/Surveys/F_0.7_dmhost_suppressed_survey.dat", +# plotfile="MC_F/Plots/F_0.7_dmhost_suppressed.pdf", +# savefile=None, +# update_params={"F": 0.7, "lmean": 1e-3, "lsigma": 0.1}, +# ) -generate( - alpha_method=1, - lum_func=2, - Nsamples=1000, - do_plots=True, - outfile="MC_F/Surveys/F_vanilla_dmhost_suppressed_survey.dat", - plotfile="MC_F/Plots/F_vanilla_dmhost_suppressed.pdf", - savefile=None, - update_params={"lmean": 1e-3, "lsigma": 0.1}, -) +# generate( +# alpha_method=1, +# lum_func=2, +# Nsamples=1000, +# do_plots=True, +# outfile="MC_F/Surveys/F_vanilla_dmhost_suppressed_survey.dat", +# plotfile="MC_F/Plots/F_vanilla_dmhost_suppressed.pdf", +# savefile=None, +# update_params={"lmean": 1e-3, "lsigma": 0.1}, +# ) diff --git a/zdm/craco/testing.py b/zdm/craco/testing.py index 31b8683d..39ab70c5 100644 --- a/zdm/craco/testing.py +++ b/zdm/craco/testing.py @@ -43,12 +43,7 @@ def main(pargs): ) # suppress DM host - igrid.update( - { - "lmean": 1e-3, - "lsigma": 0.1 - } - ) + igrid.update({"lmean": 1e-3, "lsigma": 0.1}) surveys = [isurvey] grids = [igrid] @@ -210,5 +205,9 @@ def main(pargs): python testing.py F .1 .999 --nstep 100 --nFRB 1000 -o MC_F/Plots/synth_100_F_dmhost_suppressed_0.9.png --lum_func 2 --survey ../MC_F/Surveys/F_0.9_dmhost_suppressed_survey python testing.py F .1 .999 --nstep 100 --nFRB 1000 -o MC_F/Plots/synth_100_F_dmhost_suppressed_vanilla.png --lum_func 2 --survey ../MC_F/Surveys/F_vanilla_dmhost_suppressed_survey +python testing.py H0 60. 80. --nstep 50 --nFRB 100 -o MC_Plots/CRACO_100_H0_TEST.png --lum_func 2 --survey CRACO_alpha1_Planck18_Gamma + +python testing.py H0 60. 80. --nstep 50 --nFRB 100 -o MC_Plots/CRACO_100_H0_TEST.png --lum_func 2 --survey CRACO_alpha1_Planck18_Gamma + """ From fb9a6c6bd1d074d87f8bf4b68c040aa5bea58eca Mon Sep 17 00:00:00 2001 From: Jay-MBP Date: Fri, 15 Jul 2022 13:40:09 -0700 Subject: [PATCH 015/104] point cube run to correct survey --- papers/F/Analysis/CRACO/Cloud/run_craco_mini.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/papers/F/Analysis/CRACO/Cloud/run_craco_mini.py b/papers/F/Analysis/CRACO/Cloud/run_craco_mini.py index dc992b73..5da06051 100644 --- a/papers/F/Analysis/CRACO/Cloud/run_craco_mini.py +++ b/papers/F/Analysis/CRACO/Cloud/run_craco_mini.py @@ -61,7 +61,7 @@ def main( "-o", f"{outfile}", "-s", - f"CRACO_alpha1_Planck18_Gamma", + f"../../../../../zdm/craco/MC_F/Surveys/F_0.32_survey.dat", "--clobber", "-p", f"{pfile}", From a8bdfffdef2297eed41dca19d5e655827fb56b9d Mon Sep 17 00:00:00 2001 From: Jay-MBP Date: Fri, 15 Jul 2022 13:50:26 -0700 Subject: [PATCH 016/104] adjust cube parameters for next run --- papers/F/Analysis/CRACO/Cubes/craco_mini_cube.json | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/papers/F/Analysis/CRACO/Cubes/craco_mini_cube.json b/papers/F/Analysis/CRACO/Cubes/craco_mini_cube.json index d4816c1a..74329d39 100644 --- a/papers/F/Analysis/CRACO/Cubes/craco_mini_cube.json +++ b/papers/F/Analysis/CRACO/Cubes/craco_mini_cube.json @@ -65,16 +65,16 @@ "max": 0.7, "n": 5 }, - "lC": { - "DC": "FRBdemo", - "min": -0.911, - "max": -0.911, - "n": -1 - }, "F": { "DC": "IGM", "min": 0.01, "max": 0.99, - "n": 10 + "n": 20 + }, + "lC": { + "DC": "FRBdemo", + "min": -0.911, + "max": -0.911, + "n": 1 } } \ No newline at end of file From 66efed275bb9fd9e023ee759958fcbcc8123054e Mon Sep 17 00:00:00 2001 From: Jay-MBP Date: Sat, 16 Jul 2022 14:27:35 -0700 Subject: [PATCH 017/104] debugging cube analysis; adjusting testing.py --- zdm/analyze_cube.py | 4 +- .../MC_Surveys/CRACO_F_0.32_survey_state.json | 57 +++++++++++++++++++ zdm/craco/mc.py | 4 +- zdm/craco/testing.py | 9 +-- 4 files changed, 63 insertions(+), 11 deletions(-) create mode 100644 zdm/craco/MC_Surveys/CRACO_F_0.32_survey_state.json diff --git a/zdm/analyze_cube.py b/zdm/analyze_cube.py index 83bd3a66..590c56f3 100644 --- a/zdm/analyze_cube.py +++ b/zdm/analyze_cube.py @@ -90,7 +90,7 @@ def slurp_cube( for n in ns: r_current = np.array( - [0] + list(np.unravel_index(int(n), cube_shape, order="F")) + [1] + list(np.unravel_index(int(n), cube_shape, order="F")) ) current = r_current[iorder][:-1] # Truncate lC # Ravel me back @@ -126,7 +126,7 @@ def slurp_cube( pz=pz_cube, ) - embed(header="line 129") + # embed(header="line 129") # Save the parameter values too for name in PARAMS[:-1]: diff --git a/zdm/craco/MC_Surveys/CRACO_F_0.32_survey_state.json b/zdm/craco/MC_Surveys/CRACO_F_0.32_survey_state.json new file mode 100644 index 00000000..38ba399e --- /dev/null +++ b/zdm/craco/MC_Surveys/CRACO_F_0.32_survey_state.json @@ -0,0 +1,57 @@ +{ + "FRBdemo": { + "alpha_method": 1, + "lC": 1, + "sfr_n": 0.73, + "source_evolution": 0 + }, + "IGM": { + "F": 0.32 + }, + "MW": { + "DMhalo": 50, + "ISM": 35.0 + }, + "analysis": { + "NewGrids": true, + "sprefix": "Std" + }, + "beam": { + "Bmethod": 2, + "Bthresh": 0 + }, + "cosmo": { + "H0": 67.66, + "Omega_b": 0.04897, + "Omega_b_h2": 0.0224178568132, + "Omega_k": 0.0, + "Omega_lambda": 0.6888463055445441, + "Omega_m": 0.30966, + "fix_Omega_b_h2": true + }, + "energy": { + "alpha": 0.65, + "gamma": -1.01, + "lEmax": 41.4, + "lEmin": 30, + "luminosity_function": 2 + }, + "host": { + "lmean": 2.18, + "lsigma": 0.48 + }, + "scat": { + "Sfnorm": 600, + "Sfpower": -4.0, + "Slogmean": 0.7, + "Slogsigma": 1.9 + }, + "width": { + "Wbins": 10, + "Wlogmean": 1.70267, + "Wlogsigma": 0.899148, + "Wmethod": 2, + "Wscale": 2, + "Wthresh": 0.5 + } +} \ No newline at end of file diff --git a/zdm/craco/mc.py b/zdm/craco/mc.py index 1d095c84..0ad84bd6 100644 --- a/zdm/craco/mc.py +++ b/zdm/craco/mc.py @@ -361,8 +361,8 @@ def do_basic_sample_plots(sample, opdir="Plots"): lum_func=2, Nsamples=1000, do_plots=True, - outfile="MC_F/Surveys/F_0.32_survey.dat", - plotfile="MC_F/Plots/F_0.32.pdf", + outfile="MC_Surveys/CRACO_F_0.32_survey.dat", + plotfile="MC_Plots/CRACO_F_0.32.pdf", savefile=None, update_params={"F": 0.32}, ) diff --git a/zdm/craco/testing.py b/zdm/craco/testing.py index 39ab70c5..0d3ad3d8 100644 --- a/zdm/craco/testing.py +++ b/zdm/craco/testing.py @@ -42,9 +42,6 @@ def main(pargs): lum_func=pargs.lum_func, ) - # suppress DM host - igrid.update({"lmean": 1e-3, "lsigma": 0.1}) - surveys = [isurvey] grids = [igrid] @@ -53,10 +50,6 @@ def main(pargs): vparams[pargs.param] = None vparams["lC"] = -0.9 - # DEBUGGING - # print("WARNING: REMOVE THE LINE BELOW WHEN DONE DEBUGGING") - # vparams['lEmax'] = 40.6 - lls = [] nterms = [] # LL term related to norm (i.e. rates) pvterms = [] # LL term related to norm (i.e. rates) @@ -209,5 +202,7 @@ def main(pargs): python testing.py H0 60. 80. --nstep 50 --nFRB 100 -o MC_Plots/CRACO_100_H0_TEST.png --lum_func 2 --survey CRACO_alpha1_Planck18_Gamma +python testing.py H0 60. 80. --nstep 50 --nFRB 100 -o MC_Plots/CRACO_100_H0_TEST_F32.png --lum_func 2 --survey ../MC_F/Surveys/F_0.32_survey + """ From e5e015480fde1990161fc74e57067d33a878bf9d Mon Sep 17 00:00:00 2001 From: Jay-MBP Date: Sat, 16 Jul 2022 14:29:52 -0700 Subject: [PATCH 018/104] push F=0.32 survey dat file --- zdm/craco/MC_Surveys/CRACO_F_0.32_survey.dat | 1014 ++++++++++++++++++ 1 file changed, 1014 insertions(+) create mode 100644 zdm/craco/MC_Surveys/CRACO_F_0.32_survey.dat diff --git a/zdm/craco/MC_Surveys/CRACO_F_0.32_survey.dat b/zdm/craco/MC_Surveys/CRACO_F_0.32_survey.dat new file mode 100644 index 00000000..a9ab2b88 --- /dev/null +++ b/zdm/craco/MC_Surveys/CRACO_F_0.32_survey.dat @@ -0,0 +1,1014 @@ +BW 288 #MHz +FRES 1 #MHz +DIAM 12 +NBEAMS 36 +BEAM lat50_log #prefix of beam file +FBAR 1320 +TRES 1.7 #ms +SNRTHRESH 9.5 +NFRB 1000 +NORM_FRB 1000 +TOBS 96.65 # days of 100% efficient operation +THRESH 0.99 #Jy ms to 1 ms burst [ 22 Jy ms / 24 antennas *sqrt{336/288} bandwidth factor] +KEY ID DM DMG DMEG Z SNR WIDTH +# fake data from MC simulations +FRB 0 550.1 35 465.1 0.313 25.5 2.0 +FRB 1 273.7 35 188.7 0.020 22.8 2.0 +FRB 2 680.2 35 595.2 0.645 10.2 1.0 +FRB 3 515.1 35 430.1 0.179 36.1 3.0 +FRB 4 572.3 35 487.3 0.207 44.0 5.0 +FRB 5 330.9 35 245.9 0.299 17.6 4.0 +FRB 6 888.9 35 803.9 0.737 22.3 3.0 +FRB 7 378.2 35 293.2 0.358 25.7 2.0 +FRB 8 458.5 35 373.5 0.047 104.3 1.0 +FRB 9 1047.4 35 962.4 1.259 19.8 2.0 +FRB 10 349.8 35 264.8 0.300 39.3 4.0 +FRB 11 376.2 35 291.2 0.476 15.3 2.0 +FRB 12 240.3 35 155.3 0.254 33.6 1.0 +FRB 13 495.1 35 410.1 0.486 13.1 3.0 +FRB 14 302.0 35 217.0 0.061 16.2 3.0 +FRB 15 465.4 35 380.4 0.222 46.8 3.0 +FRB 16 298.9 35 213.9 0.173 12.0 3.0 +FRB 17 362.7 35 277.7 0.074 12.0 4.0 +FRB 18 1707.5 35 1622.5 0.842 16.8 3.0 +FRB 19 122.8 35 37.8 0.003 53.0 2.0 +FRB 20 405.5 35 320.5 0.538 58.0 3.0 +FRB 21 896.8 35 811.8 0.754 13.1 3.0 +FRB 22 300.8 35 215.8 0.163 19.6 2.0 +FRB 23 913.3 35 828.3 0.768 10.6 2.0 +FRB 24 763.0 35 678.0 0.716 31.8 3.0 +FRB 25 1107.1 35 1022.1 1.020 13.2 2.0 +FRB 26 417.6 35 332.6 0.352 138.9 1.0 +FRB 27 355.0 35 270.0 0.283 14.8 2.0 +FRB 28 240.7 35 155.7 0.038 18.7 3.0 +FRB 29 618.0 35 533.0 0.318 37.9 4.0 +FRB 30 975.5 35 890.5 0.407 17.5 2.0 +FRB 31 403.9 35 318.9 0.124 10.0 3.0 +FRB 32 975.2 35 890.2 1.093 11.3 2.0 +FRB 33 142.8 35 57.8 0.001 20.2 4.0 +FRB 34 299.6 35 214.6 0.208 16.5 1.0 +FRB 35 243.5 35 158.5 0.050 10.1 0.0 +FRB 36 1455.2 35 1370.2 1.873 10.6 2.0 +FRB 37 178.4 35 93.4 0.082 68.0 2.0 +FRB 38 719.1 35 634.1 0.927 10.1 4.0 +FRB 39 795.7 35 710.7 0.495 11.8 1.0 +FRB 40 397.4 35 312.4 0.347 10.4 2.0 +FRB 41 350.3 35 265.3 0.111 18.2 2.0 +FRB 42 218.6 35 133.6 0.092 12.9 1.0 +FRB 43 332.3 35 247.3 0.179 29.6 3.0 +FRB 44 435.0 35 350.0 0.256 16.5 3.0 +FRB 45 568.2 35 483.2 0.463 11.9 1.0 +FRB 46 1062.3 35 977.3 1.071 18.1 1.0 +FRB 47 401.7 35 316.7 0.293 30.1 2.0 +FRB 48 589.2 35 504.2 0.503 13.1 1.0 +FRB 49 244.4 35 159.4 0.014 36.6 3.0 +FRB 50 223.5 35 138.5 0.224 193.2 3.0 +FRB 51 1308.0 35 1223.0 0.453 10.3 2.0 +FRB 52 384.5 35 299.5 0.286 23.9 1.0 +FRB 53 678.3 35 593.3 0.588 10.5 3.0 +FRB 54 488.8 35 403.8 0.333 9.8 1.0 +FRB 55 418.4 35 333.4 0.382 12.0 2.0 +FRB 56 388.3 35 303.3 0.354 55.5 4.0 +FRB 57 479.7 35 394.7 0.135 55.6 2.0 +FRB 58 176.3 35 91.3 0.078 40.5 2.0 +FRB 59 328.3 35 243.3 0.277 16.8 1.0 +FRB 60 577.7 35 492.7 0.683 38.5 2.0 +FRB 61 509.6 35 424.6 0.359 11.7 0.0 +FRB 62 398.7 35 313.7 0.483 18.9 2.0 +FRB 63 691.3 35 606.3 0.197 20.8 2.0 +FRB 64 493.2 35 408.2 0.374 15.2 3.0 +FRB 65 350.9 35 265.9 0.028 93.5 3.0 +FRB 66 428.1 35 343.1 0.435 16.8 4.0 +FRB 67 801.0 35 716.0 1.053 16.2 4.0 +FRB 68 412.7 35 327.7 0.172 15.2 2.0 +FRB 69 907.4 35 822.4 0.497 26.8 1.0 +FRB 70 461.0 35 376.0 0.272 125.7 3.0 +FRB 71 943.3 35 858.3 0.638 16.8 3.0 +FRB 72 419.5 35 334.5 0.234 59.2 1.0 +FRB 73 187.7 35 102.7 0.064 168.5 2.0 +FRB 74 551.6 35 466.6 0.459 26.4 3.0 +FRB 75 276.6 35 191.6 0.288 25.6 0.0 +FRB 76 275.7 35 190.7 0.141 28.0 3.0 +FRB 77 421.4 35 336.4 0.097 13.7 1.0 +FRB 78 345.1 35 260.1 0.222 11.3 1.0 +FRB 79 476.7 35 391.7 0.191 15.6 3.0 +FRB 80 277.7 35 192.7 0.055 645.9 1.0 +FRB 81 411.0 35 326.0 0.333 12.3 2.0 +FRB 82 654.8 35 569.8 0.060 16.8 4.0 +FRB 83 440.5 35 355.5 0.101 20.4 0.0 +FRB 84 355.8 35 270.8 0.184 89.8 3.0 +FRB 85 627.5 35 542.5 0.428 13.5 5.0 +FRB 86 1497.3 35 1412.3 1.597 10.1 3.0 +FRB 87 220.6 35 135.6 0.127 16.7 3.0 +FRB 88 219.8 35 134.8 0.104 10.2 1.0 +FRB 89 193.1 35 108.1 0.127 15.0 3.0 +FRB 90 532.5 35 447.5 0.490 13.4 2.0 +FRB 91 1473.7 35 1388.7 0.689 12.6 3.0 +FRB 92 528.4 35 443.4 0.256 9.6 2.0 +FRB 93 886.6 35 801.6 0.942 10.3 1.0 +FRB 94 558.4 35 473.4 0.827 39.2 2.0 +FRB 95 751.5 35 666.5 0.788 19.6 2.0 +FRB 96 365.8 35 280.8 0.071 48.4 3.0 +FRB 97 442.8 35 357.8 0.290 10.4 2.0 +FRB 98 808.5 35 723.5 0.421 11.3 2.0 +FRB 99 1026.1 35 941.1 1.304 14.3 4.0 +FRB 100 186.7 35 101.7 0.150 15.9 2.0 +FRB 101 1179.5 35 1094.5 1.321 26.0 1.0 +FRB 102 438.5 35 353.5 0.062 20.2 4.0 +FRB 103 315.3 35 230.3 0.089 17.9 1.0 +FRB 104 833.1 35 748.1 0.949 10.5 2.0 +FRB 105 595.3 35 510.3 0.250 32.8 2.0 +FRB 106 313.4 35 228.4 0.370 16.8 4.0 +FRB 107 568.5 35 483.5 0.629 13.1 2.0 +FRB 108 143.5 35 58.5 0.026 11.0 4.0 +FRB 109 743.4 35 658.4 0.812 12.6 3.0 +FRB 110 941.9 35 856.9 0.755 10.0 2.0 +FRB 111 460.5 35 375.5 0.355 10.7 2.0 +FRB 112 1271.8 35 1186.8 0.432 12.1 2.0 +FRB 113 1308.6 35 1223.6 1.273 15.7 2.0 +FRB 114 567.9 35 482.9 0.608 12.4 3.0 +FRB 115 410.5 35 325.5 0.057 84.7 2.0 +FRB 116 391.1 35 306.1 0.251 117.3 1.0 +FRB 117 1287.8 35 1202.8 1.592 9.7 2.0 +FRB 118 634.8 35 549.8 0.378 15.9 2.0 +FRB 119 383.0 35 298.0 0.176 15.6 2.0 +FRB 120 372.7 35 287.7 0.167 16.2 2.0 +FRB 121 237.3 35 152.3 0.041 13.3 2.0 +FRB 122 478.8 35 393.8 0.354 27.0 2.0 +FRB 123 835.0 35 750.0 0.735 51.2 2.0 +FRB 124 282.6 35 197.6 0.263 10.6 3.0 +FRB 125 151.4 35 66.4 0.046 223.4 3.0 +FRB 126 819.9 35 734.9 0.750 13.1 4.0 +FRB 127 162.3 35 77.3 0.138 17.3 3.0 +FRB 128 371.1 35 286.1 0.282 25.0 1.0 +FRB 129 357.4 35 272.4 0.262 16.3 1.0 +FRB 130 331.8 35 246.8 0.085 40.2 3.0 +FRB 131 557.5 35 472.5 0.289 88.5 3.0 +FRB 132 818.5 35 733.5 0.660 9.8 3.0 +FRB 133 1257.6 35 1172.6 0.699 22.4 3.0 +FRB 134 1116.3 35 1031.3 1.367 11.6 2.0 +FRB 135 2259.2 35 2174.2 1.338 12.3 3.0 +FRB 136 307.9 35 222.9 0.243 22.6 4.0 +FRB 137 1311.1 35 1226.1 1.712 24.4 3.0 +FRB 138 848.2 35 763.2 1.006 11.6 2.0 +FRB 139 1060.6 35 975.6 1.108 10.6 3.0 +FRB 140 785.5 35 700.5 0.164 10.0 3.0 +FRB 141 484.9 35 399.9 0.520 11.7 0.0 +FRB 142 481.2 35 396.2 0.424 9.6 3.0 +FRB 143 484.7 35 399.7 0.610 14.9 3.0 +FRB 144 260.6 35 175.6 0.054 12.9 3.0 +FRB 145 393.8 35 308.8 0.291 12.7 4.0 +FRB 146 273.7 35 188.7 0.182 22.7 3.0 +FRB 147 534.4 35 449.4 0.556 21.0 3.0 +FRB 148 703.6 35 618.6 0.195 10.8 1.0 +FRB 149 335.9 35 250.9 0.329 18.5 3.0 +FRB 150 898.1 35 813.1 0.718 32.2 2.0 +FRB 151 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724.0 0.973 15.2 3.0 +FRB 968 200.8 35 115.8 0.017 40.9 1.0 +FRB 969 624.5 35 539.5 0.608 29.0 3.0 +FRB 970 765.5 35 680.5 0.903 27.9 3.0 +FRB 971 723.4 35 638.4 0.748 18.6 2.0 +FRB 972 1077.4 35 992.4 0.677 17.1 2.0 +FRB 973 817.6 35 732.6 0.672 11.1 4.0 +FRB 974 1497.4 35 1412.4 1.515 10.0 4.0 +FRB 975 569.8 35 484.8 0.124 57.6 3.0 +FRB 976 510.3 35 425.3 0.480 33.4 2.0 +FRB 977 452.5 35 367.5 0.265 10.3 4.0 +FRB 978 975.6 35 890.6 1.173 11.2 3.0 +FRB 979 496.8 35 411.8 0.375 55.7 1.0 +FRB 980 199.7 35 114.7 0.038 9.9 3.0 +FRB 981 806.6 35 721.6 0.794 14.6 2.0 +FRB 982 642.3 35 557.3 0.439 13.4 3.0 +FRB 983 959.5 35 874.5 0.787 16.6 0.0 +FRB 984 396.7 35 311.7 0.330 21.8 3.0 +FRB 985 142.1 35 57.1 0.043 21.8 3.0 +FRB 986 376.1 35 291.1 0.219 19.0 1.0 +FRB 987 1077.2 35 992.2 1.389 35.2 1.0 +FRB 988 632.7 35 547.7 0.475 11.9 0.0 +FRB 989 330.1 35 245.1 0.163 11.7 3.0 +FRB 990 247.1 35 162.1 0.152 18.0 3.0 +FRB 991 140.6 35 55.6 0.019 20.3 3.0 +FRB 992 578.6 35 493.6 0.301 53.0 1.0 +FRB 993 230.2 35 145.2 0.125 10.1 2.0 +FRB 994 420.1 35 335.1 0.135 18.6 3.0 +FRB 995 579.4 35 494.4 0.360 14.9 1.0 +FRB 996 1031.4 35 946.4 1.190 14.7 2.0 +FRB 997 391.9 35 306.9 0.147 20.3 5.0 +FRB 998 341.4 35 256.4 0.267 79.0 1.0 +FRB 999 442.7 35 357.7 0.433 14.0 3.0 From 38858d54ae97978f3337b05209026a81936e5328 Mon Sep 17 00:00:00 2001 From: Jay-MBP Date: Mon, 18 Jul 2022 15:00:51 -0700 Subject: [PATCH 019/104] force f=0.32 survey dat --- zdm/craco/MC_F/Surveys/F_0.32_survey.dat | 1014 ++++++++++++++++++++++ 1 file changed, 1014 insertions(+) create mode 100644 zdm/craco/MC_F/Surveys/F_0.32_survey.dat diff --git a/zdm/craco/MC_F/Surveys/F_0.32_survey.dat b/zdm/craco/MC_F/Surveys/F_0.32_survey.dat new file mode 100644 index 00000000..a9ab2b88 --- /dev/null +++ b/zdm/craco/MC_F/Surveys/F_0.32_survey.dat @@ -0,0 +1,1014 @@ +BW 288 #MHz +FRES 1 #MHz +DIAM 12 +NBEAMS 36 +BEAM lat50_log #prefix of beam file +FBAR 1320 +TRES 1.7 #ms +SNRTHRESH 9.5 +NFRB 1000 +NORM_FRB 1000 +TOBS 96.65 # days of 100% efficient operation +THRESH 0.99 #Jy ms to 1 ms burst [ 22 Jy ms / 24 antennas *sqrt{336/288} bandwidth factor] +KEY ID DM DMG DMEG Z SNR WIDTH +# fake data from MC simulations +FRB 0 550.1 35 465.1 0.313 25.5 2.0 +FRB 1 273.7 35 188.7 0.020 22.8 2.0 +FRB 2 680.2 35 595.2 0.645 10.2 1.0 +FRB 3 515.1 35 430.1 0.179 36.1 3.0 +FRB 4 572.3 35 487.3 0.207 44.0 5.0 +FRB 5 330.9 35 245.9 0.299 17.6 4.0 +FRB 6 888.9 35 803.9 0.737 22.3 3.0 +FRB 7 378.2 35 293.2 0.358 25.7 2.0 +FRB 8 458.5 35 373.5 0.047 104.3 1.0 +FRB 9 1047.4 35 962.4 1.259 19.8 2.0 +FRB 10 349.8 35 264.8 0.300 39.3 4.0 +FRB 11 376.2 35 291.2 0.476 15.3 2.0 +FRB 12 240.3 35 155.3 0.254 33.6 1.0 +FRB 13 495.1 35 410.1 0.486 13.1 3.0 +FRB 14 302.0 35 217.0 0.061 16.2 3.0 +FRB 15 465.4 35 380.4 0.222 46.8 3.0 +FRB 16 298.9 35 213.9 0.173 12.0 3.0 +FRB 17 362.7 35 277.7 0.074 12.0 4.0 +FRB 18 1707.5 35 1622.5 0.842 16.8 3.0 +FRB 19 122.8 35 37.8 0.003 53.0 2.0 +FRB 20 405.5 35 320.5 0.538 58.0 3.0 +FRB 21 896.8 35 811.8 0.754 13.1 3.0 +FRB 22 300.8 35 215.8 0.163 19.6 2.0 +FRB 23 913.3 35 828.3 0.768 10.6 2.0 +FRB 24 763.0 35 678.0 0.716 31.8 3.0 +FRB 25 1107.1 35 1022.1 1.020 13.2 2.0 +FRB 26 417.6 35 332.6 0.352 138.9 1.0 +FRB 27 355.0 35 270.0 0.283 14.8 2.0 +FRB 28 240.7 35 155.7 0.038 18.7 3.0 +FRB 29 618.0 35 533.0 0.318 37.9 4.0 +FRB 30 975.5 35 890.5 0.407 17.5 2.0 +FRB 31 403.9 35 318.9 0.124 10.0 3.0 +FRB 32 975.2 35 890.2 1.093 11.3 2.0 +FRB 33 142.8 35 57.8 0.001 20.2 4.0 +FRB 34 299.6 35 214.6 0.208 16.5 1.0 +FRB 35 243.5 35 158.5 0.050 10.1 0.0 +FRB 36 1455.2 35 1370.2 1.873 10.6 2.0 +FRB 37 178.4 35 93.4 0.082 68.0 2.0 +FRB 38 719.1 35 634.1 0.927 10.1 4.0 +FRB 39 795.7 35 710.7 0.495 11.8 1.0 +FRB 40 397.4 35 312.4 0.347 10.4 2.0 +FRB 41 350.3 35 265.3 0.111 18.2 2.0 +FRB 42 218.6 35 133.6 0.092 12.9 1.0 +FRB 43 332.3 35 247.3 0.179 29.6 3.0 +FRB 44 435.0 35 350.0 0.256 16.5 3.0 +FRB 45 568.2 35 483.2 0.463 11.9 1.0 +FRB 46 1062.3 35 977.3 1.071 18.1 1.0 +FRB 47 401.7 35 316.7 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35 126.2 0.103 29.9 3.0 +FRB 891 807.2 35 722.2 0.785 10.5 2.0 +FRB 892 438.7 35 353.7 0.478 14.2 4.0 +FRB 893 175.2 35 90.2 0.023 17.4 1.0 +FRB 894 350.0 35 265.0 0.313 12.3 3.0 +FRB 895 1865.2 35 1780.2 1.939 10.4 2.0 +FRB 896 560.0 35 475.0 0.468 18.4 2.0 +FRB 897 198.2 35 113.2 0.039 233.6 2.0 +FRB 898 585.4 35 500.4 0.667 11.1 2.0 +FRB 899 347.7 35 262.7 0.258 13.7 2.0 +FRB 900 756.3 35 671.3 0.889 10.6 1.0 +FRB 901 210.4 35 125.4 0.010 11.4 0.0 +FRB 902 656.0 35 571.0 0.508 10.1 4.0 +FRB 903 1539.0 35 1454.0 2.030 15.9 2.0 +FRB 904 137.6 35 52.6 0.016 14.6 2.0 +FRB 905 480.9 35 395.9 0.081 45.1 2.0 +FRB 906 506.7 35 421.7 0.425 42.2 2.0 +FRB 907 479.0 35 394.0 0.264 13.5 4.0 +FRB 908 910.5 35 825.5 1.062 12.0 1.0 +FRB 909 764.0 35 679.0 1.017 25.4 1.0 +FRB 910 358.5 35 273.5 0.041 43.9 4.0 +FRB 911 100.5 35 15.5 0.007 92.1 3.0 +FRB 912 403.5 35 318.5 0.327 13.4 2.0 +FRB 913 349.2 35 264.2 0.161 32.6 4.0 +FRB 914 1399.6 35 1314.6 0.970 9.7 0.0 +FRB 915 130.8 35 45.8 0.049 27.4 2.0 +FRB 916 184.5 35 99.5 0.092 10.6 4.0 +FRB 917 553.7 35 468.7 0.241 10.1 3.0 +FRB 918 465.2 35 380.2 0.547 25.5 2.0 +FRB 919 627.3 35 542.3 0.410 31.8 2.0 +FRB 920 629.9 35 544.9 0.010 77.8 4.0 +FRB 921 779.1 35 694.1 0.449 60.8 2.0 +FRB 922 511.7 35 426.7 0.154 12.0 2.0 +FRB 923 224.6 35 139.6 0.012 51.8 3.0 +FRB 924 389.8 35 304.8 0.122 42.0 3.0 +FRB 925 661.0 35 576.0 0.818 15.7 2.0 +FRB 926 993.1 35 908.1 0.237 11.5 3.0 +FRB 927 1479.7 35 1394.7 1.498 9.7 3.0 +FRB 928 476.2 35 391.2 0.517 22.0 3.0 +FRB 929 414.2 35 329.2 0.170 37.4 3.0 +FRB 930 319.1 35 234.1 0.201 15.1 2.0 +FRB 931 1336.7 35 1251.7 1.526 10.8 3.0 +FRB 932 1027.7 35 942.7 1.055 13.1 3.0 +FRB 933 545.3 35 460.3 0.388 26.9 3.0 +FRB 934 369.4 35 284.4 0.010 10.7 1.0 +FRB 935 168.6 35 83.6 0.003 29.4 2.0 +FRB 936 1013.5 35 928.5 1.012 13.2 2.0 +FRB 937 254.8 35 169.8 0.184 14.9 3.0 +FRB 938 1715.0 35 1630.0 2.012 12.1 2.0 +FRB 939 2046.3 35 1961.3 1.186 13.0 0.0 +FRB 940 194.4 35 109.4 0.031 35.6 2.0 +FRB 941 235.0 35 150.0 0.218 149.4 3.0 +FRB 942 636.0 35 551.0 0.467 12.9 3.0 +FRB 943 260.7 35 175.7 0.118 15.2 3.0 +FRB 944 923.6 35 838.6 0.803 18.1 3.0 +FRB 945 913.5 35 828.5 1.208 24.7 1.0 +FRB 946 200.0 35 115.0 0.155 11.0 3.0 +FRB 947 363.2 35 278.2 0.386 12.0 3.0 +FRB 948 324.1 35 239.1 0.178 22.7 1.0 +FRB 949 497.9 35 412.9 0.549 24.1 3.0 +FRB 950 464.8 35 379.8 0.503 10.6 4.0 +FRB 951 897.2 35 812.2 0.893 19.2 3.0 +FRB 952 243.3 35 158.3 0.111 12.5 2.0 +FRB 953 340.6 35 255.6 0.173 9.9 2.0 +FRB 954 239.4 35 154.4 0.240 11.0 3.0 +FRB 955 672.0 35 587.0 0.618 12.1 3.0 +FRB 956 918.3 35 833.3 0.450 19.9 3.0 +FRB 957 351.4 35 266.4 0.394 27.7 3.0 +FRB 958 865.6 35 780.6 1.064 10.3 0.0 +FRB 959 1634.9 35 1549.9 1.946 15.9 3.0 +FRB 960 375.6 35 290.6 0.255 9.6 1.0 +FRB 961 1282.2 35 1197.2 1.503 12.7 3.0 +FRB 962 810.4 35 725.4 0.109 22.6 3.0 +FRB 963 1615.8 35 1530.8 0.363 14.0 4.0 +FRB 964 410.2 35 325.2 0.229 14.9 2.0 +FRB 965 322.5 35 237.5 0.171 37.2 4.0 +FRB 966 807.4 35 722.4 0.926 20.6 3.0 +FRB 967 809.0 35 724.0 0.973 15.2 3.0 +FRB 968 200.8 35 115.8 0.017 40.9 1.0 +FRB 969 624.5 35 539.5 0.608 29.0 3.0 +FRB 970 765.5 35 680.5 0.903 27.9 3.0 +FRB 971 723.4 35 638.4 0.748 18.6 2.0 +FRB 972 1077.4 35 992.4 0.677 17.1 2.0 +FRB 973 817.6 35 732.6 0.672 11.1 4.0 +FRB 974 1497.4 35 1412.4 1.515 10.0 4.0 +FRB 975 569.8 35 484.8 0.124 57.6 3.0 +FRB 976 510.3 35 425.3 0.480 33.4 2.0 +FRB 977 452.5 35 367.5 0.265 10.3 4.0 +FRB 978 975.6 35 890.6 1.173 11.2 3.0 +FRB 979 496.8 35 411.8 0.375 55.7 1.0 +FRB 980 199.7 35 114.7 0.038 9.9 3.0 +FRB 981 806.6 35 721.6 0.794 14.6 2.0 +FRB 982 642.3 35 557.3 0.439 13.4 3.0 +FRB 983 959.5 35 874.5 0.787 16.6 0.0 +FRB 984 396.7 35 311.7 0.330 21.8 3.0 +FRB 985 142.1 35 57.1 0.043 21.8 3.0 +FRB 986 376.1 35 291.1 0.219 19.0 1.0 +FRB 987 1077.2 35 992.2 1.389 35.2 1.0 +FRB 988 632.7 35 547.7 0.475 11.9 0.0 +FRB 989 330.1 35 245.1 0.163 11.7 3.0 +FRB 990 247.1 35 162.1 0.152 18.0 3.0 +FRB 991 140.6 35 55.6 0.019 20.3 3.0 +FRB 992 578.6 35 493.6 0.301 53.0 1.0 +FRB 993 230.2 35 145.2 0.125 10.1 2.0 +FRB 994 420.1 35 335.1 0.135 18.6 3.0 +FRB 995 579.4 35 494.4 0.360 14.9 1.0 +FRB 996 1031.4 35 946.4 1.190 14.7 2.0 +FRB 997 391.9 35 306.9 0.147 20.3 5.0 +FRB 998 341.4 35 256.4 0.267 79.0 1.0 +FRB 999 442.7 35 357.7 0.433 14.0 3.0 From 321adb0380dff398acb7386b33722be6a5394781 Mon Sep 17 00:00:00 2001 From: profxj Date: Mon, 18 Jul 2022 15:36:40 -0700 Subject: [PATCH 020/104] fixed n=-1 --- papers/F/Analysis/CRACO/Cloud/run_craco_mini.py | 6 +++++- .../F/Analysis/CRACO/Cubes/craco_mini_cube.json | 2 +- zdm/craco/testing.py | 16 ++++++++++++++++ 3 files changed, 22 insertions(+), 2 deletions(-) diff --git a/papers/F/Analysis/CRACO/Cloud/run_craco_mini.py b/papers/F/Analysis/CRACO/Cloud/run_craco_mini.py index 5da06051..07eff00c 100644 --- a/papers/F/Analysis/CRACO/Cloud/run_craco_mini.py +++ b/papers/F/Analysis/CRACO/Cloud/run_craco_mini.py @@ -5,6 +5,7 @@ import argparse import numpy as np import os, sys +from pkg_resources import resource_filename from concurrent.futures import ProcessPoolExecutor import subprocess @@ -45,6 +46,8 @@ def main( if int(ntotal / total_ncpu) != nper_cpu: raise IOError(f"Ncpu={total_ncpu} must divide evenly into ntotal={ntotal}") + survey_file = os.path.join(resource_filename('zdm', 'craco'), + 'MC_F', 'Surveys', 'F_0.32_survey') commands = [] for kk in range(pargs.ncpu): line = [] @@ -61,7 +64,7 @@ def main( "-o", f"{outfile}", "-s", - f"../../../../../zdm/craco/MC_F/Surveys/F_0.32_survey.dat", + f"{survey_file}", "--clobber", "-p", f"{pfile}", @@ -78,6 +81,7 @@ def main( # Launch em! processes = [] + embed(header='84 of run craco') for command in commands: # Popen print(f"Running this command: {' '.join(command)}") diff --git a/papers/F/Analysis/CRACO/Cubes/craco_mini_cube.json b/papers/F/Analysis/CRACO/Cubes/craco_mini_cube.json index 74329d39..cedf3118 100644 --- a/papers/F/Analysis/CRACO/Cubes/craco_mini_cube.json +++ b/papers/F/Analysis/CRACO/Cubes/craco_mini_cube.json @@ -75,6 +75,6 @@ "DC": "FRBdemo", "min": -0.911, "max": -0.911, - "n": 1 + "n": -1 } } \ No newline at end of file diff --git a/zdm/craco/testing.py b/zdm/craco/testing.py index 0d3ad3d8..2d143681 100644 --- a/zdm/craco/testing.py +++ b/zdm/craco/testing.py @@ -14,6 +14,8 @@ import matplotlib from matplotlib import pyplot as plt +import pandas + from zdm import iteration as it from zdm.craco import loading @@ -50,6 +52,17 @@ def main(pargs): vparams[pargs.param] = None vparams["lC"] = -0.9 + ''' + tparams = pandas.read_csv('tst_params.csv') + for key in ['lEmax', 'alpha','gamma','sfr_n','lmean','lsigma','F']: + vparams[key] = tparams[key][0] + tmp_dict = { + 'lEmax': 40.5, 'H0': 64.375, 'alpha': 0.2, 'gamma': -0.5, + 'sfr_n': 0.0, 'lmean': 1.7, 'lsigma': 0.3, 'F': 0.11} + vparams.update(tmp_dict) + #embed(header='64 of testing') + ''' + lls = [] nterms = [] # LL term related to norm (i.e. rates) pvterms = [] # LL term related to norm (i.e. rates) @@ -204,5 +217,8 @@ def main(pargs): python testing.py H0 60. 80. --nstep 50 --nFRB 100 -o MC_Plots/CRACO_100_H0_TEST_F32.png --lum_func 2 --survey ../MC_F/Surveys/F_0.32_survey +# More fussing about with F and related +python testing.py H0 60. 80. --nstep 50 --nFRB 100 -o MC_Plots/CRACO_100_H0_TEST_F32.png --lum_func 2 --survey ../MC_F/Surveys/F_0.32_survey + """ From f7c330e8c730cdbf17d4d1621aeb69f82bd9c244 Mon Sep 17 00:00:00 2001 From: profxj Date: Mon, 18 Jul 2022 16:13:51 -0700 Subject: [PATCH 021/104] grid mod --- zdm/grid.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/zdm/grid.py b/zdm/grid.py index 4f6a7c6e..5875297a 100644 --- a/zdm/grid.py +++ b/zdm/grid.py @@ -631,7 +631,7 @@ def update(self, vparams:dict, ALL=False, prev_grid=None): smear_dm = True calc_thresh = True calc_pdv = True - set_evol = True + set_evol = True # JXP THINKS THIS SHOULD BE FALSE new_sfr_smear = True # DM_host From 7778494da84799d3a5650853c0f51db0b47f4581 Mon Sep 17 00:00:00 2001 From: Jay-MBP Date: Wed, 20 Jul 2022 10:49:40 -0700 Subject: [PATCH 022/104] fix bug in analyze_cube; adjust grid.update for F --- .../Analysis/CRACO/Cubes/craco_mini_cube.json | 4 +- .../F/Analysis/CRACO/py/craco_qck_explore.py | 61 +- zdm/analyze_cube.py | 32 +- ...F_0.01_dmhost_suppressed_survey_state.json | 57 ++ .../MC_F/Surveys/F_0.01_survey_state.json | 57 ++ .../MC_F/Surveys/F_0.32_survey_state.json | 57 ++ .../F_0.7_dmhost_suppressed_survey_state.json | 57 ++ .../MC_F/Surveys/F_0.7_survey_state.json | 57 ++ .../F_0.9_dmhost_suppressed_survey_state.json | 57 ++ .../MC_F/Surveys/F_0.9_survey_state.json | 57 ++ ...anilla_dmhost_suppressed_survey_state.json | 57 ++ .../MC_F/Surveys/F_vanilla_survey_state.json | 57 ++ zdm/grid.py | 767 ++++++++++-------- 13 files changed, 985 insertions(+), 392 deletions(-) create mode 100644 zdm/craco/MC_F/Surveys/F_0.01_dmhost_suppressed_survey_state.json create mode 100644 zdm/craco/MC_F/Surveys/F_0.01_survey_state.json create mode 100644 zdm/craco/MC_F/Surveys/F_0.32_survey_state.json create mode 100644 zdm/craco/MC_F/Surveys/F_0.7_dmhost_suppressed_survey_state.json create mode 100644 zdm/craco/MC_F/Surveys/F_0.7_survey_state.json create mode 100644 zdm/craco/MC_F/Surveys/F_0.9_dmhost_suppressed_survey_state.json create mode 100644 zdm/craco/MC_F/Surveys/F_0.9_survey_state.json create mode 100644 zdm/craco/MC_F/Surveys/F_vanilla_dmhost_suppressed_survey_state.json create mode 100644 zdm/craco/MC_F/Surveys/F_vanilla_survey_state.json diff --git a/papers/F/Analysis/CRACO/Cubes/craco_mini_cube.json b/papers/F/Analysis/CRACO/Cubes/craco_mini_cube.json index 74329d39..71384436 100644 --- a/papers/F/Analysis/CRACO/Cubes/craco_mini_cube.json +++ b/papers/F/Analysis/CRACO/Cubes/craco_mini_cube.json @@ -69,12 +69,12 @@ "DC": "IGM", "min": 0.01, "max": 0.99, - "n": 20 + "n": 15 }, "lC": { "DC": "FRBdemo", "min": -0.911, "max": -0.911, - "n": 1 + "n": -1 } } \ No newline at end of file diff --git a/papers/F/Analysis/CRACO/py/craco_qck_explore.py b/papers/F/Analysis/CRACO/py/craco_qck_explore.py index 748fb812..cd4169f2 100644 --- a/papers/F/Analysis/CRACO/py/craco_qck_explore.py +++ b/papers/F/Analysis/CRACO/py/craco_qck_explore.py @@ -11,42 +11,42 @@ from IPython import embed -#sys.path.append(os.path.abspath("../../Figures/py")) +# sys.path.append(os.path.abspath("../../Figures/py")) + def main(pargs): jroot = None - if pargs.run == 'mini': - scube = 'mini' - outdir = 'Mini/' - elif pargs.run == 'full': - scube = 'full' - outdir = 'Full/' - elif pargs.run == 'full400': - scube = '400_full' - jroot = 'full' - outdir = 'Full400/' - elif pargs.run == 'full3rd': - scube = '3rd_full' - jroot = 'full' - outdir = 'Full3rd/' + if pargs.run == "mini": + scube = "mini" + outdir = "Mini/" + elif pargs.run == "full": + scube = "full" + outdir = "Full/" + elif pargs.run == "full400": + scube = "400_full" + jroot = "full" + outdir = "Full400/" + elif pargs.run == "full3rd": + scube = "3rd_full" + jroot = "full" + outdir = "Full3rd/" if jroot is None: jroot = scube - # Load - npdict = np.load(f'Cubes/craco_{scube}_cube.npz') + npdict = np.load(f"Cubes/craco_{scube}_cube.npz") - ll_cube = npdict['ll'] + ll_cube = npdict["ll"] # Deal with Nan ll_cube[np.isnan(ll_cube)] = -1e99 - params = npdict['params'] + params = npdict["params"] # Cube parameters ############## Load up ############## - pfile = f'Cubes/craco_{jroot}_cube.json' - input_dict=io.process_jfile(pfile) + pfile = f"Cubes/craco_{jroot}_cube.json" + input_dict = io.process_jfile(pfile) # Deconstruct the input_dict state_dict, cube_dict, vparam_dict = it.parse_input_dict(input_dict) @@ -56,14 +56,14 @@ def main(pargs): # Offset by max ll_cube = ll_cube - np.max(ll_cube) - uvals,vectors,wvectors = analyze_cube.get_bayesian_data(ll_cube) + uvals, vectors, wvectors = analyze_cube.get_bayesian_data(ll_cube) - embed(header="Debugging...") - - analyze_cube.do_single_plots(uvals,vectors,wvectors, params, - vparams_dict=vparam_dict, outdir=outdir) + analyze_cube.do_single_plots( + uvals, vectors, wvectors, params, vparams_dict=vparam_dict, outdir=outdir + ) print(f"Wrote figures to {outdir}") + def parse_option(): """ This is a function used to parse the arguments in the training. @@ -75,16 +75,17 @@ def parse_option(): parser = argparse.ArgumentParser("Slurping the cubes") parser.add_argument("run", type=str, help="Run to slurp") - #parser.add_argument('--debug', default=False, action='store_true', + # parser.add_argument('--debug', default=False, action='store_true', # help='Debug?') args = parser.parse_args() - + return args + # Command line execution -if __name__ == '__main__': +if __name__ == "__main__": pargs = parse_option() main(pargs) -# python py/slurp_craco_cubes.py mini \ No newline at end of file +# python py/slurp_craco_cubes.py mini diff --git a/zdm/analyze_cube.py b/zdm/analyze_cube.py index 590c56f3..03502bff 100644 --- a/zdm/analyze_cube.py +++ b/zdm/analyze_cube.py @@ -48,7 +48,7 @@ def slurp_cube( order, iorder = iteration.set_orders(cube_dict["parameter_order"], PARAMS) cube_shape = iteration.set_cube_shape(vparam_dict, order) - param_shape = np.array([1] + cube_shape)[iorder].tolist()[:-1] + param_shape = np.array([0] + cube_shape)[iorder].tolist()[:-1] # Outputs ll_cube = np.zeros(param_shape, dtype=np.float32) @@ -90,7 +90,7 @@ def slurp_cube( for n in ns: r_current = np.array( - [1] + list(np.unravel_index(int(n), cube_shape, order="F")) + [0] + list(np.unravel_index(int(n), cube_shape, order="F")) ) current = r_current[iorder][:-1] # Truncate lC # Ravel me back @@ -905,18 +905,6 @@ def do_single_plots( for i, vals in enumerate(uvals): - kind = None - - if len(vals) == 1: - continue - if len(vals) < 4: - kind = "linear" - else: - kind = "cubic" - # does the for alpha - plt.figure() - lw = 3 - # Convert vals? if vparams_dict is not None: # Check @@ -924,7 +912,10 @@ def do_single_plots( vals = np.linspace( vparams_dict[names[i]]["min"], vparams_dict[names[i]]["max"], len(vals) ) - + + plt.figure() + lw = 3 + # get raw ylimits # removes zeroes, could lead to strange behaviour in theory ymax = np.max(vectors[i]) @@ -934,6 +925,17 @@ def do_single_plots( ymax = math.ceil(ymax) ymin = 0.0 + kind = None + + if len(vals[temp]) == 1: + continue + if len(vals[temp]) < 4: + kind = "linear" + else: + kind = "cubic" + # does the for alpha + + x, y = interpolate_points(vals[temp], vectors[i][temp], logspline, kind=kind) norm = np.sum(y) * (x[1] - x[0]) # integral y dx ~ sum y delta x diff --git a/zdm/craco/MC_F/Surveys/F_0.01_dmhost_suppressed_survey_state.json b/zdm/craco/MC_F/Surveys/F_0.01_dmhost_suppressed_survey_state.json new file mode 100644 index 00000000..1a2786ba --- /dev/null +++ b/zdm/craco/MC_F/Surveys/F_0.01_dmhost_suppressed_survey_state.json @@ -0,0 +1,57 @@ +{ + "FRBdemo": { + "alpha_method": 1, + "lC": 1, + "sfr_n": 0.73, + "source_evolution": 0 + }, + "IGM": { + "F": 0.01 + }, + "MW": { + "DMhalo": 50, + "ISM": 35.0 + }, + "analysis": { + "NewGrids": true, + "sprefix": "Std" + }, + "beam": { + "Bmethod": 2, + "Bthresh": 0 + }, + "cosmo": { + "H0": 67.66, + "Omega_b": 0.04897, + "Omega_b_h2": 0.0224178568132, + "Omega_k": 0.0, + "Omega_lambda": 0.6888463055445441, + "Omega_m": 0.30966, + "fix_Omega_b_h2": true + }, + "energy": { + "alpha": 0.65, + "gamma": -1.01, + "lEmax": 41.4, + "lEmin": 30, + "luminosity_function": 2 + }, + "host": { + "lmean": 0.001, + "lsigma": 0.1 + }, + "scat": { + "Sfnorm": 600, + "Sfpower": -4.0, + "Slogmean": 0.7, + "Slogsigma": 1.9 + }, + "width": { + "Wbins": 10, + "Wlogmean": 1.70267, + "Wlogsigma": 0.899148, + "Wmethod": 2, + "Wscale": 2, + "Wthresh": 0.5 + } +} \ No newline at end of file diff --git a/zdm/craco/MC_F/Surveys/F_0.01_survey_state.json b/zdm/craco/MC_F/Surveys/F_0.01_survey_state.json new file mode 100644 index 00000000..d1394e1c --- /dev/null +++ b/zdm/craco/MC_F/Surveys/F_0.01_survey_state.json @@ -0,0 +1,57 @@ +{ + "FRBdemo": { + "alpha_method": 1, + "lC": 1, + "sfr_n": 0.73, + "source_evolution": 0 + }, + "IGM": { + "F": 0.01 + }, + "MW": { + "DMhalo": 50, + "ISM": 35.0 + }, + "analysis": { + "NewGrids": true, + "sprefix": "Std" + }, + "beam": { + "Bmethod": 2, + "Bthresh": 0 + }, + "cosmo": { + "H0": 67.66, + "Omega_b": 0.04897, + "Omega_b_h2": 0.0224178568132, + "Omega_k": 0.0, + "Omega_lambda": 0.6888463055445441, + "Omega_m": 0.30966, + "fix_Omega_b_h2": true + }, + "energy": { + "alpha": 0.65, + "gamma": -1.01, + "lEmax": 41.4, + "lEmin": 30, + "luminosity_function": 2 + }, + "host": { + "lmean": 2.18, + "lsigma": 0.48 + }, + "scat": { + "Sfnorm": 600, + "Sfpower": -4.0, + "Slogmean": 0.7, + "Slogsigma": 1.9 + }, + "width": { + "Wbins": 10, + "Wlogmean": 1.70267, + "Wlogsigma": 0.899148, + "Wmethod": 2, + "Wscale": 2, + "Wthresh": 0.5 + } +} \ No newline at end of file diff --git a/zdm/craco/MC_F/Surveys/F_0.32_survey_state.json b/zdm/craco/MC_F/Surveys/F_0.32_survey_state.json new file mode 100644 index 00000000..38ba399e --- /dev/null +++ b/zdm/craco/MC_F/Surveys/F_0.32_survey_state.json @@ -0,0 +1,57 @@ +{ + "FRBdemo": { + "alpha_method": 1, + "lC": 1, + "sfr_n": 0.73, + "source_evolution": 0 + }, + "IGM": { + "F": 0.32 + }, + "MW": { + "DMhalo": 50, + "ISM": 35.0 + }, + "analysis": { + "NewGrids": true, + "sprefix": "Std" + }, + "beam": { + "Bmethod": 2, + "Bthresh": 0 + }, + "cosmo": { + "H0": 67.66, + "Omega_b": 0.04897, + "Omega_b_h2": 0.0224178568132, + "Omega_k": 0.0, + "Omega_lambda": 0.6888463055445441, + "Omega_m": 0.30966, + "fix_Omega_b_h2": true + }, + "energy": { + "alpha": 0.65, + "gamma": -1.01, + "lEmax": 41.4, + "lEmin": 30, + "luminosity_function": 2 + }, + "host": { + "lmean": 2.18, + "lsigma": 0.48 + }, + "scat": { + "Sfnorm": 600, + "Sfpower": -4.0, + "Slogmean": 0.7, + "Slogsigma": 1.9 + }, + "width": { + "Wbins": 10, + "Wlogmean": 1.70267, + "Wlogsigma": 0.899148, + "Wmethod": 2, + "Wscale": 2, + "Wthresh": 0.5 + } +} \ No newline at end of file diff --git a/zdm/craco/MC_F/Surveys/F_0.7_dmhost_suppressed_survey_state.json b/zdm/craco/MC_F/Surveys/F_0.7_dmhost_suppressed_survey_state.json new file mode 100644 index 00000000..85364587 --- /dev/null +++ b/zdm/craco/MC_F/Surveys/F_0.7_dmhost_suppressed_survey_state.json @@ -0,0 +1,57 @@ +{ + "FRBdemo": { + "alpha_method": 1, + "lC": 1, + "sfr_n": 0.73, + "source_evolution": 0 + }, + "IGM": { + "F": 0.7 + }, + "MW": { + "DMhalo": 50, + "ISM": 35.0 + }, + "analysis": { + "NewGrids": true, + "sprefix": "Std" + }, + "beam": { + "Bmethod": 2, + "Bthresh": 0 + }, + "cosmo": { + "H0": 67.66, + "Omega_b": 0.04897, + "Omega_b_h2": 0.0224178568132, + "Omega_k": 0.0, + "Omega_lambda": 0.6888463055445441, + "Omega_m": 0.30966, + "fix_Omega_b_h2": true + }, + "energy": { + "alpha": 0.65, + "gamma": -1.01, + "lEmax": 41.4, + "lEmin": 30, + "luminosity_function": 2 + }, + "host": { + "lmean": 0.001, + "lsigma": 0.1 + }, + "scat": { + "Sfnorm": 600, + "Sfpower": -4.0, + "Slogmean": 0.7, + "Slogsigma": 1.9 + }, + "width": { + "Wbins": 10, + "Wlogmean": 1.70267, + "Wlogsigma": 0.899148, + "Wmethod": 2, + "Wscale": 2, + "Wthresh": 0.5 + } +} \ No newline at end of file diff --git a/zdm/craco/MC_F/Surveys/F_0.7_survey_state.json b/zdm/craco/MC_F/Surveys/F_0.7_survey_state.json new file mode 100644 index 00000000..9b158036 --- /dev/null +++ b/zdm/craco/MC_F/Surveys/F_0.7_survey_state.json @@ -0,0 +1,57 @@ +{ + "FRBdemo": { + "alpha_method": 1, + "lC": 1, + "sfr_n": 0.73, + "source_evolution": 0 + }, + "IGM": { + "F": 0.7 + }, + "MW": { + "DMhalo": 50, + "ISM": 35.0 + }, + "analysis": { + "NewGrids": true, + "sprefix": "Std" + }, + "beam": { + "Bmethod": 2, + "Bthresh": 0 + }, + "cosmo": { + "H0": 67.66, + "Omega_b": 0.04897, + "Omega_b_h2": 0.0224178568132, + "Omega_k": 0.0, + "Omega_lambda": 0.6888463055445441, + "Omega_m": 0.30966, + "fix_Omega_b_h2": true + }, + "energy": { + "alpha": 0.65, + "gamma": -1.01, + "lEmax": 41.4, + "lEmin": 30, + "luminosity_function": 2 + }, + "host": { + "lmean": 2.18, + "lsigma": 0.48 + }, + "scat": { + "Sfnorm": 600, + "Sfpower": -4.0, + "Slogmean": 0.7, + "Slogsigma": 1.9 + }, + "width": { + "Wbins": 10, + "Wlogmean": 1.70267, + "Wlogsigma": 0.899148, + "Wmethod": 2, + "Wscale": 2, + "Wthresh": 0.5 + } +} \ No newline at end of file diff --git a/zdm/craco/MC_F/Surveys/F_0.9_dmhost_suppressed_survey_state.json b/zdm/craco/MC_F/Surveys/F_0.9_dmhost_suppressed_survey_state.json new file mode 100644 index 00000000..22a1f8b1 --- /dev/null +++ b/zdm/craco/MC_F/Surveys/F_0.9_dmhost_suppressed_survey_state.json @@ -0,0 +1,57 @@ +{ + "FRBdemo": { + "alpha_method": 1, + "lC": 1, + "sfr_n": 0.73, + "source_evolution": 0 + }, + "IGM": { + "F": 0.9 + }, + "MW": { + "DMhalo": 50, + "ISM": 35.0 + }, + "analysis": { + "NewGrids": true, + "sprefix": "Std" + }, + "beam": { + "Bmethod": 2, + "Bthresh": 0 + }, + "cosmo": { + "H0": 67.66, + "Omega_b": 0.04897, + "Omega_b_h2": 0.0224178568132, + "Omega_k": 0.0, + "Omega_lambda": 0.6888463055445441, + "Omega_m": 0.30966, + "fix_Omega_b_h2": true + }, + "energy": { + "alpha": 0.65, + "gamma": -1.01, + "lEmax": 41.4, + "lEmin": 30, + "luminosity_function": 2 + }, + "host": { + "lmean": 0.001, + "lsigma": 0.1 + }, + "scat": { + "Sfnorm": 600, + "Sfpower": -4.0, + "Slogmean": 0.7, + "Slogsigma": 1.9 + }, + "width": { + "Wbins": 10, + "Wlogmean": 1.70267, + "Wlogsigma": 0.899148, + "Wmethod": 2, + "Wscale": 2, + "Wthresh": 0.5 + } +} \ No newline at end of file diff --git a/zdm/craco/MC_F/Surveys/F_0.9_survey_state.json b/zdm/craco/MC_F/Surveys/F_0.9_survey_state.json new file mode 100644 index 00000000..80d4aa07 --- /dev/null +++ b/zdm/craco/MC_F/Surveys/F_0.9_survey_state.json @@ -0,0 +1,57 @@ +{ + "FRBdemo": { + "alpha_method": 1, + "lC": 1, + "sfr_n": 0.73, + "source_evolution": 0 + }, + "IGM": { + "F": 0.9 + }, + "MW": { + "DMhalo": 50, + "ISM": 35.0 + }, + "analysis": { + "NewGrids": true, + "sprefix": "Std" + }, + "beam": { + "Bmethod": 2, + "Bthresh": 0 + }, + "cosmo": { + "H0": 67.66, + "Omega_b": 0.04897, + "Omega_b_h2": 0.0224178568132, + "Omega_k": 0.0, + "Omega_lambda": 0.6888463055445441, + "Omega_m": 0.30966, + "fix_Omega_b_h2": true + }, + "energy": { + "alpha": 0.65, + "gamma": -1.01, + "lEmax": 41.4, + "lEmin": 30, + "luminosity_function": 2 + }, + "host": { + "lmean": 2.18, + "lsigma": 0.48 + }, + "scat": { + "Sfnorm": 600, + "Sfpower": -4.0, + "Slogmean": 0.7, + "Slogsigma": 1.9 + }, + "width": { + "Wbins": 10, + "Wlogmean": 1.70267, + "Wlogsigma": 0.899148, + "Wmethod": 2, + "Wscale": 2, + "Wthresh": 0.5 + } +} \ No newline at end of file diff --git a/zdm/craco/MC_F/Surveys/F_vanilla_dmhost_suppressed_survey_state.json b/zdm/craco/MC_F/Surveys/F_vanilla_dmhost_suppressed_survey_state.json new file mode 100644 index 00000000..dca5c55f --- /dev/null +++ b/zdm/craco/MC_F/Surveys/F_vanilla_dmhost_suppressed_survey_state.json @@ -0,0 +1,57 @@ +{ + "FRBdemo": { + "alpha_method": 1, + "lC": 1, + "sfr_n": 0.73, + "source_evolution": 0 + }, + "IGM": { + "F": 0.32 + }, + "MW": { + "DMhalo": 50, + "ISM": 35.0 + }, + "analysis": { + "NewGrids": true, + "sprefix": "Std" + }, + "beam": { + "Bmethod": 2, + "Bthresh": 0 + }, + "cosmo": { + "H0": 67.66, + "Omega_b": 0.04897, + "Omega_b_h2": 0.0224178568132, + "Omega_k": 0.0, + "Omega_lambda": 0.6888463055445441, + "Omega_m": 0.30966, + "fix_Omega_b_h2": true + }, + "energy": { + "alpha": 0.65, + "gamma": -1.01, + "lEmax": 41.4, + "lEmin": 30, + "luminosity_function": 2 + }, + "host": { + "lmean": 0.001, + "lsigma": 0.1 + }, + "scat": { + "Sfnorm": 600, + "Sfpower": -4.0, + "Slogmean": 0.7, + "Slogsigma": 1.9 + }, + "width": { + "Wbins": 10, + "Wlogmean": 1.70267, + "Wlogsigma": 0.899148, + "Wmethod": 2, + "Wscale": 2, + "Wthresh": 0.5 + } +} \ No newline at end of file diff --git a/zdm/craco/MC_F/Surveys/F_vanilla_survey_state.json b/zdm/craco/MC_F/Surveys/F_vanilla_survey_state.json new file mode 100644 index 00000000..38ba399e --- /dev/null +++ b/zdm/craco/MC_F/Surveys/F_vanilla_survey_state.json @@ -0,0 +1,57 @@ +{ + "FRBdemo": { + "alpha_method": 1, + "lC": 1, + "sfr_n": 0.73, + "source_evolution": 0 + }, + "IGM": { + "F": 0.32 + }, + "MW": { + "DMhalo": 50, + "ISM": 35.0 + }, + "analysis": { + "NewGrids": true, + "sprefix": "Std" + }, + "beam": { + "Bmethod": 2, + "Bthresh": 0 + }, + "cosmo": { + "H0": 67.66, + "Omega_b": 0.04897, + "Omega_b_h2": 0.0224178568132, + "Omega_k": 0.0, + "Omega_lambda": 0.6888463055445441, + "Omega_m": 0.30966, + "fix_Omega_b_h2": true + }, + "energy": { + "alpha": 0.65, + "gamma": -1.01, + "lEmax": 41.4, + "lEmin": 30, + "luminosity_function": 2 + }, + "host": { + "lmean": 2.18, + "lsigma": 0.48 + }, + "scat": { + "Sfnorm": 600, + "Sfpower": -4.0, + "Slogmean": 0.7, + "Slogsigma": 1.9 + }, + "width": { + "Wbins": 10, + "Wlogmean": 1.70267, + "Wlogsigma": 0.899148, + "Wmethod": 2, + "Wscale": 2, + "Wthresh": 0.5 + } +} \ No newline at end of file diff --git a/zdm/grid.py b/zdm/grid.py index 4f6a7c6e..85db06ad 100644 --- a/zdm/grid.py +++ b/zdm/grid.py @@ -8,6 +8,7 @@ from zdm import pcosmic from zdm import io + class Grid: """A class to hold a grid of z-dm plots @@ -16,10 +17,8 @@ class Grid: It also assumes a linear uniform grid. """ - - def __init__(self, survey, state, - zDMgrid, zvals, dmvals, smear_mask, - wdist): + + def __init__(self, survey, state, zDMgrid, zvals, dmvals, smear_mask, wdist): """ Class constructor. @@ -32,110 +31,111 @@ def __init__(self, survey, state, wdist (bool): If True, allow for a distribution of widths """ - self.grid=None + self.grid = None self.survey = survey - self.verbose=False + self.verbose = False # Beam - self.beam_b=survey.beam_b - self.beam_o=survey.beam_o - self.b_fractions=None + self.beam_b = survey.beam_b + self.beam_o = survey.beam_o + self.b_fractions = None # State self.state = state - self.source_function=cos.choose_source_evolution_function( - state.FRBdemo.source_evolution) + self.source_function = cos.choose_source_evolution_function( + state.FRBdemo.source_evolution + ) self.luminosity_function = self.state.energy.luminosity_function self.init_luminosity_functions() - - self.nuObs=survey.meta['FBAR']*1e6 #from MHz to Hz + + self.nuObs = survey.meta["FBAR"] * 1e6 # from MHz to Hz # Init the grid # THESE SHOULD BE THE SAME ORDER AS self.update() - self.parse_grid(zDMgrid.copy(),zvals.copy(),dmvals.copy()) + self.parse_grid(zDMgrid.copy(), zvals.copy(), dmvals.copy()) self.calc_dV() self.smear_dm(smear_mask.copy()) if wdist: - efficiencies=survey.efficiencies # two dimensions - weights=survey.wplist + efficiencies = survey.efficiencies # two dimensions + weights = survey.wplist else: - efficiencies=survey.mean_efficiencies - weights=None - self.efficiencies=efficiencies - self.weights=weights - self.calc_thresholds(survey.meta['THRESH'], - efficiencies, - weights=weights) + efficiencies = survey.mean_efficiencies + weights = None + self.efficiencies = efficiencies + self.weights = weights + self.calc_thresholds(survey.meta["THRESH"], efficiencies, weights=weights) self.calc_pdv() - self.set_evolution() # sets star-formation rate scaling with z - here, no evoltion... - self.calc_rates() #includes sfr smearing factors and pdv mult + self.set_evolution() # sets star-formation rate scaling with z - here, no evoltion... + self.calc_rates() # includes sfr smearing factors and pdv mult def init_luminosity_functions(self): """ Set the luminsoity function for FRB energetics """ - if self.luminosity_function==0: # Power-law - self.array_cum_lf=energetics.array_cum_power_law - self.vector_cum_lf=energetics.vector_cum_power_law - self.array_diff_lf=energetics.array_diff_power_law - self.vector_diff_lf=energetics.vector_diff_power_law - elif self.luminosity_function==1: # Gamma function - embed(header='79 of grid -- BEST NOT TO USE THIS!!!!') - self.array_cum_lf=energetics.array_cum_gamma - self.vector_cum_lf=energetics.vector_cum_gamma - self.array_diff_lf=energetics.array_diff_gamma - self.vector_diff_lf=energetics.vector_diff_gamma - elif self.luminosity_function==2: # Spline gamma function - self.array_cum_lf=energetics.array_cum_gamma_spline - self.vector_cum_lf=energetics.vector_cum_gamma_spline - self.array_diff_lf=energetics.array_diff_gamma - self.vector_diff_lf=energetics.vector_diff_gamma + if self.luminosity_function == 0: # Power-law + self.array_cum_lf = energetics.array_cum_power_law + self.vector_cum_lf = energetics.vector_cum_power_law + self.array_diff_lf = energetics.array_diff_power_law + self.vector_diff_lf = energetics.vector_diff_power_law + elif self.luminosity_function == 1: # Gamma function + embed(header="79 of grid -- BEST NOT TO USE THIS!!!!") + self.array_cum_lf = energetics.array_cum_gamma + self.vector_cum_lf = energetics.vector_cum_gamma + self.array_diff_lf = energetics.array_diff_gamma + self.vector_diff_lf = energetics.vector_diff_gamma + elif self.luminosity_function == 2: # Spline gamma function + self.array_cum_lf = energetics.array_cum_gamma_spline + self.vector_cum_lf = energetics.vector_cum_gamma_spline + self.array_diff_lf = energetics.array_diff_gamma + self.vector_diff_lf = energetics.vector_diff_gamma # Init else: - raise ValueError("Luminosity function must be 0, not ",self.luminosity_function) - - def parse_grid(self,zDMgrid,zvals,dmvals): - self.grid=zDMgrid - self.zvals=zvals - self.dmvals=dmvals + raise ValueError( + "Luminosity function must be 0, not ", self.luminosity_function + ) + + def parse_grid(self, zDMgrid, zvals, dmvals): + self.grid = zDMgrid + self.zvals = zvals + self.dmvals = dmvals # self.check_grid() - #self.calc_dV() - + # self.calc_dV() + # this contains all the values used to generate grids # these parameters begin at None, and get filled when # ever something is regenerated. They are semi-hierarchical # in that if a low-level value is reset, high-level ones # get put to None. - - - - def load_grid(self,gridfile,zfile,dmfile): - self.grid=io.load_data(gridfile) - self.zvals=io.load_data(zfile) - self.dmvals=io.load_data(dmfile) + + def load_grid(self, gridfile, zfile, dmfile): + self.grid = io.load_data(gridfile) + self.zvals = io.load_data(zfile) + self.dmvals = io.load_data(dmfile) self.check_grid() self.volume_grid() - - + def check_grid(self): - - self.nz=self.zvals.size - self.ndm=self.dmvals.size - + + self.nz = self.zvals.size + self.ndm = self.dmvals.size + # check to see if these are log-spaced - if (self.zvals[-1]-self.zvals[-2])/(self.zvals[1]-self.zvals[0]) > 1.01: - if np.abs(self.zvals[-1]*self.zvals[0] - self.zvals[-2]*self.zvals[1]) > 0.01: + if (self.zvals[-1] - self.zvals[-2]) / (self.zvals[1] - self.zvals[0]) > 1.01: + if ( + np.abs(self.zvals[-1] * self.zvals[0] - self.zvals[-2] * self.zvals[1]) + > 0.01 + ): raise ValueError("Cannot determine scaling of zvals, exiting...") - self.zlog=True - self.dz=np.log(self.zvals[1]/self.zvals[0]) + self.zlog = True + self.dz = np.log(self.zvals[1] / self.zvals[0]) else: - self.zlog=False - self.dz=self.zvals[1]-self.zvals[0] - - self.ddm=self.dmvals[1]-self.dmvals[0] - shape=self.grid.shape + self.zlog = False + self.dz = self.zvals[1] - self.zvals[0] + + self.ddm = self.dmvals[1] - self.dmvals[0] + shape = self.grid.shape if shape[0] != self.nz: if shape[0] == self.ndm and shape[1] == self.nz: print("Transposing grid, looks like first index is DM") - self.grid=self.grid.transpose + self.grid = self.grid.transpose else: raise ValueError("wrong shape of grid for zvals and dm vals") else: @@ -144,66 +144,81 @@ def check_grid(self): print("Grid successfully initialised") else: raise ValueError("wrong shape of grid for zvals and dm vals") - - - #checks that the grid is approximately linear to high precision + + # checks that the grid is approximately linear to high precision if self.zlog: - expectation=np.exp(np.arange(0,self.nz)*self.dz)*self.zvals[0] + expectation = np.exp(np.arange(0, self.nz) * self.dz) * self.zvals[0] else: - expectation=self.dz*np.arange(0,self.nz)+self.zvals[0] - diff=self.zvals-expectation - maxoff=np.max(diff**2) - if maxoff > 1e-6*self.dz: - raise ValueError("Maximum non-linearity in z-grid of ",maxoff**0.5,"detected, aborting") - - expectation=self.ddm*np.arange(0,self.ndm)+self.dmvals[0] - diff=self.dmvals-expectation - maxoff=np.max(diff**2) - if maxoff > 1e-6*self.ddm: - raise ValueError("Maximum non-linearity in dm-grid of ",maxoff**0.5,"detected, aborting") - - + expectation = self.dz * np.arange(0, self.nz) + self.zvals[0] + diff = self.zvals - expectation + maxoff = np.max(diff ** 2) + if maxoff > 1e-6 * self.dz: + raise ValueError( + "Maximum non-linearity in z-grid of ", + maxoff ** 0.5, + "detected, aborting", + ) + + expectation = self.ddm * np.arange(0, self.ndm) + self.dmvals[0] + diff = self.dmvals - expectation + maxoff = np.max(diff ** 2) + if maxoff > 1e-6 * self.ddm: + raise ValueError( + "Maximum non-linearity in dm-grid of ", + maxoff ** 0.5, + "detected, aborting", + ) + def calc_dV(self, reINIT=False): """ Calculates volume per steradian probed by a survey. Does this only in the z-dimension (for obvious reasons!) """ - + if (cos.INIT is False) or reINIT: - #print('WARNING: cosmology not yet initiated, using default parameters.') + # print('WARNING: cosmology not yet initiated, using default parameters.') cos.init_dist_measures() if self.zlog: # if zlog, dz is actually .dlogz. And dlogz/dz=1/z, i.e. dz= z dlogz - self.dV=cos.dvdtau(self.zvals)*self.dz*self.zvals + self.dV = cos.dvdtau(self.zvals) * self.dz * self.zvals else: - self.dV=cos.dvdtau(self.zvals)*self.dz - - - def EF(self,alpha=0,bandwidth=1e9): + self.dV = cos.dvdtau(self.zvals) * self.dz + + def EF(self, alpha=0, bandwidth=1e9): """Calculates the fluence--energy conversion factors as a function of redshift This does NOT account for the central frequency """ - if self.state.FRBdemo.alpha_method==0: - self.FtoE=cos.F_to_E(1,self.zvals,alpha=alpha,bandwidth=bandwidth,Fobs=self.nuObs,Fref=self.nuRef) - elif self.state.FRBdemo.alpha_method==1: - self.FtoE=cos.F_to_E(1,self.zvals,alpha=0.,bandwidth=bandwidth) + if self.state.FRBdemo.alpha_method == 0: + self.FtoE = cos.F_to_E( + 1, + self.zvals, + alpha=alpha, + bandwidth=bandwidth, + Fobs=self.nuObs, + Fref=self.nuRef, + ) + elif self.state.FRBdemo.alpha_method == 1: + self.FtoE = cos.F_to_E(1, self.zvals, alpha=0.0, bandwidth=bandwidth) else: - raise ValueError("alpha method must be 0 or 1, not ",self.alpha_method) - - def set_evolution(self): #,n,alpha=None): + raise ValueError("alpha method must be 0 or 1, not ", self.alpha_method) + + def set_evolution(self): # ,n,alpha=None): """ Scales volumetric rate by SFR """ - #self.sfr1n=n - #if alpha is not None: + # self.sfr1n=n + # if alpha is not None: # self.alpha=alpha - #self.sfr=cos.sfr(self.zvals)**n #old hard-coded value - self.sfr=self.source_function(self.zvals, - self.state.FRBdemo.sfr_n) - if self.state.FRBdemo.alpha_method==1: - self.sfr *= (1.+self.zvals)**(-self.state.energy.alpha) #reduces rate with alpha + # self.sfr=cos.sfr(self.zvals)**n #old hard-coded value + self.sfr = self.source_function(self.zvals, self.state.FRBdemo.sfr_n) + if self.state.FRBdemo.alpha_method == 1: + self.sfr *= (1.0 + self.zvals) ** ( + -self.state.energy.alpha + ) # reduces rate with alpha # changes absolute normalisation at z=0 according to central frequency - self.sfr *= (self.nuObs/self.nuRef)**-self.state.energy.alpha #alpha positive, nuObs dm)[0][0] - DM1=DM2-1 - kDM=(dm-self.dmvals[DM1])/(self.dmvals[DM2]-self.dmvals[DM1]) - priors[i,:]=kDM*self.rates[:,DM2]+(1.-kDM)*self.rates[:,DM1] - priors[i,:] /= np.sum(priors[i,:]) + priors = np.zeros([DMs.size, self.zvals.size]) + for i, dm in enumerate(DMs): + DM2 = np.where(self.dmvals > dm)[0][0] + DM1 = DM2 - 1 + kDM = (dm - self.dmvals[DM1]) / (self.dmvals[DM2] - self.dmvals[DM1]) + priors[i, :] = kDM * self.rates[:, DM2] + (1.0 - kDM) * self.rates[:, DM1] + priors[i, :] /= np.sum(priors[i, :]) return priors - - def GenMCSample(self,N,Poisson=False): + + def GenMCSample(self, N, Poisson=False): """ Generate a MC sample of FRB events @@ -393,27 +435,27 @@ def GenMCSample(self,N,Poisson=False): """ # Boost? - if self.state.energy.luminosity_function in [1,2]: - Emax_boost = 2. + if self.state.energy.luminosity_function in [1, 2]: + Emax_boost = 2.0 else: - Emax_boost = 0. - + Emax_boost = 0.0 + if Poisson: - #from np.random import poisson - NFRB=np.random.poisson(N) + # from np.random import poisson + NFRB = np.random.poisson(N) else: - NFRB=int(N) #just to be sure... - sample=[] - pwb=None #feeds this back to save time. Lots of time. + NFRB = int(N) # just to be sure... + sample = [] + pwb = None # feeds this back to save time. Lots of time. for i in np.arange(NFRB): if (i % 100) == 0: print(i) - frb,pwb=self.GenMCFRB(pwb, Emax_boost=Emax_boost) + frb, pwb = self.GenMCFRB(pwb, Emax_boost=Emax_boost) sample.append(frb) - sample=np.array(sample) + sample = np.array(sample) return sample - - def GenMCFRB(self,pwb=None, Emax_boost=0.): + + def GenMCFRB(self, pwb=None, Emax_boost=0.0): """ Generates a single FRB according to the grid distributions @@ -436,130 +478,142 @@ def GenMCFRB(self,pwb=None, Emax_boost=0.): Returns: tuple: FRBparams=[MCz,MCDM,MCb,j,MCs], pwb values """ - - # shorthand - lEmin=self.state.energy.lEmin - lEmax=self.state.energy.lEmax - gamma=self.state.energy.gamma - Emin=10**lEmin - Emax=10**lEmax - - # grid of beam values, weights - nw=self.eff_weights.size - nb=self.beam_b.size - + + # shorthand + lEmin = self.state.energy.lEmin + lEmax = self.state.energy.lEmax + gamma = self.state.energy.gamma + Emin = 10 ** lEmin + Emax = 10 ** lEmax + + # grid of beam values, weights + nw = self.eff_weights.size + nb = self.beam_b.size + # we do this to allow efficient recalculation of this when generating many FRBs if pwb is not None: - pwbc=np.cumsum(pwb) - pwbc/=pwbc[-1] + pwbc = np.cumsum(pwb) + pwbc /= pwbc[-1] else: - pwb=np.zeros([nw*nb]) - + pwb = np.zeros([nw * nb]) + # Generates a joint distribution in B,w - for i,b in enumerate(self.beam_b): - for j,w in enumerate(self.eff_weights): + for i, b in enumerate(self.beam_b): + for j, w in enumerate(self.eff_weights): # each of the following is a 2D array over DM, z which we sum to generate B,w values - wb_fraction=self.beam_o[i]*w*self.array_cum_lf(self.thresholds[j,:,:]/b,Emin,Emax,gamma) - pdv=np.multiply(wb_fraction.T,self.dV).T - rate=pdv*self.sfr_smear - pwb[i*nw+j]=np.sum(rate) - pwbc=np.cumsum(pwb) - pwbc/=pwbc[-1] - + wb_fraction = ( + self.beam_o[i] + * w + * self.array_cum_lf( + self.thresholds[j, :, :] / b, Emin, Emax, gamma + ) + ) + pdv = np.multiply(wb_fraction.T, self.dV).T + rate = pdv * self.sfr_smear + pwb[i * nw + j] = np.sum(rate) + pwbc = np.cumsum(pwb) + pwbc /= pwbc[-1] + # sample distribution in w,b # we do NOT interpolate here - we treat these as qualitative values # i.e. as if there was an irregular grid of them - r=np.random.rand(1)[0] - which=np.where(pwbc>r)[0][0] - i=int(which/nw) - j=which-i*nw - MCb=self.beam_b[i] - MCw=self.eff_weights[j] - + r = np.random.rand(1)[0] + which = np.where(pwbc > r)[0][0] + i = int(which / nw) + j = which - i * nw + MCb = self.beam_b[i] + MCw = self.eff_weights[j] + # calculate zdm distribution for sampled w,b only - pzDM=self.array_cum_lf(self.thresholds[j,:,:]/MCb,Emin,Emax,gamma) - wb_fraction=self.array_cum_lf(self.thresholds[j,:,:]/MCb,Emin,Emax,gamma) - pdv=np.multiply(wb_fraction.T,self.dV).T - pzDM=pdv*self.sfr_smear - - + pzDM = self.array_cum_lf(self.thresholds[j, :, :] / MCb, Emin, Emax, gamma) + wb_fraction = self.array_cum_lf( + self.thresholds[j, :, :] / MCb, Emin, Emax, gamma + ) + pdv = np.multiply(wb_fraction.T, self.dV).T + pzDM = pdv * self.sfr_smear + # sample distribution in z,DM - pz=np.sum(pzDM,axis=1) - pzc=np.cumsum(pz) + pz = np.sum(pzDM, axis=1) + pzc = np.cumsum(pz) pzc /= pzc[-1] - r=np.random.rand(1)[0] - iz2=np.where(pzc>r)[0][0] + r = np.random.rand(1)[0] + iz2 = np.where(pzc > r)[0][0] if iz2 > 0: - iz1=iz2-1 - dr=r-pzc[iz1] - kz2=dr/(pzc[iz2]-pzc[iz1]) # fraction of way to second value - kz1=1.-kz2 - MCz=self.zvals[iz1]*kz1+self.zvals[iz2]*kz2 - pDM=pzDM[iz1,:]*kz1 + pzDM[iz2,:]*kz2 + iz1 = iz2 - 1 + dr = r - pzc[iz1] + kz2 = dr / (pzc[iz2] - pzc[iz1]) # fraction of way to second value + kz1 = 1.0 - kz2 + MCz = self.zvals[iz1] * kz1 + self.zvals[iz2] * kz2 + pDM = pzDM[iz1, :] * kz1 + pzDM[iz2, :] * kz2 else: # we perform a simple linear interpolation in z from 0 to minimum bin - kz2=r/pzc[iz2] - kz1=1.-kz2 - MCz=self.zvals[iz2]*kz2 - pDM=pzDM[iz2,:] # just use the value of lowest bin - - + kz2 = r / pzc[iz2] + kz1 = 1.0 - kz2 + MCz = self.zvals[iz2] * kz2 + pDM = pzDM[iz2, :] # just use the value of lowest bin + # NOW DO dm - #pDM=pzDM[k,:] - pDMc=np.cumsum(pDM) + # pDM=pzDM[k,:] + pDMc = np.cumsum(pDM) pDMc /= pDMc[-1] - r=np.random.rand(1)[0] - iDM2=np.where(pDMc>r)[0][0] + r = np.random.rand(1)[0] + iDM2 = np.where(pDMc > r)[0][0] if iDM2 > 0: - iDM1=iDM2-1 - dDM=r-pDMc[iDM1] - kDM2=dDM/(pDMc[iDM2] - pDMc[iDM1]) - kDM1=1.-kDM2 - MCDM=self.dmvals[iDM1]*kDM1 + self.dmvals[iDM2]*kDM2 - if iz2>0: - Eth=self.thresholds[j,iz1,iDM1]*kz1*kDM1 \ - + self.thresholds[j,iz1,iDM2]*kz1*kDM2 \ - + self.thresholds[j,iz2,iDM1]*kz2*kDM1 \ - + self.thresholds[j,iz2,iDM2]*kz2*kDM2 - else: - Eth=self.thresholds[j,iz2,iDM1]*kDM1 \ - + self.thresholds[j,iz2,iDM2]*kDM2 - Eth *= kz2**2 #assume threshold goes as Eth~z^2 in the near Universe + iDM1 = iDM2 - 1 + dDM = r - pDMc[iDM1] + kDM2 = dDM / (pDMc[iDM2] - pDMc[iDM1]) + kDM1 = 1.0 - kDM2 + MCDM = self.dmvals[iDM1] * kDM1 + self.dmvals[iDM2] * kDM2 + if iz2 > 0: + Eth = ( + self.thresholds[j, iz1, iDM1] * kz1 * kDM1 + + self.thresholds[j, iz1, iDM2] * kz1 * kDM2 + + self.thresholds[j, iz2, iDM1] * kz2 * kDM1 + + self.thresholds[j, iz2, iDM2] * kz2 * kDM2 + ) + else: + Eth = ( + self.thresholds[j, iz2, iDM1] * kDM1 + + self.thresholds[j, iz2, iDM2] * kDM2 + ) + Eth *= kz2 ** 2 # assume threshold goes as Eth~z^2 in the near Universe else: # interpolate linearly from 0 to the minimum value - kDM2=r/pDMc[iDM2] - MCDM=self.dmvals[iDM2]*kDM2 - if iz2>0: # ignore effect of lowest DM bin on threshold - Eth=self.thresholds[j,iz1,iDM2]*kz1 \ - + self.thresholds[j,iz2,iDM2]*kz2 - else: - Eth=self.thresholds[j,iz2,iDM2]*kDM2 - Eth *= kz2**2 #assume threshold goes as Eth~z^2 in the near Universe - + kDM2 = r / pDMc[iDM2] + MCDM = self.dmvals[iDM2] * kDM2 + if iz2 > 0: # ignore effect of lowest DM bin on threshold + Eth = ( + self.thresholds[j, iz1, iDM2] * kz1 + + self.thresholds[j, iz2, iDM2] * kz2 + ) + else: + Eth = self.thresholds[j, iz2, iDM2] * kDM2 + Eth *= kz2 ** 2 # assume threshold goes as Eth~z^2 in the near Universe + # now account for beamshape Eth /= MCb - + # NOW GET snr - #Eth=self.thresholds[j,k,l]/MCb - Es=np.logspace(np.log10(Eth),np.log10(Emax) + Emax_boost, 1000) - PEs=self.vector_cum_lf(Es,Emin,Emax,gamma) - PEs /= PEs[0] # normalises: this is now cumulative distribution from 1 to 0 - r=np.random.rand(1)[0] - iE1=np.where(PEs>r)[0][-1] #returns list starting at 0 and going upwards - iE2 = iE1+1 + # Eth=self.thresholds[j,k,l]/MCb + Es = np.logspace(np.log10(Eth), np.log10(Emax) + Emax_boost, 1000) + PEs = self.vector_cum_lf(Es, Emin, Emax, gamma) + PEs /= PEs[0] # normalises: this is now cumulative distribution from 1 to 0 + r = np.random.rand(1)[0] + iE1 = np.where(PEs > r)[0][-1] # returns list starting at 0 and going upwards + iE2 = iE1 + 1 # iE1 should never be the highest energy, since it will always have a probability of 0 (or near 0 for Gamma) - kE1=(r-PEs[iE2])/(PEs[iE1]-PEs[iE2]) - kE2=1.-kE1 - MCE=10**(np.log10(Es[iE1])*kE1 + np.log10(Es[iE2])*kE2) - MCs=MCE/Eth - - FRBparams=[MCz,MCDM,MCb,j,MCs] - return FRBparams,pwb - + kE1 = (r - PEs[iE2]) / (PEs[iE1] - PEs[iE2]) + kE2 = 1.0 - kE1 + MCE = 10 ** (np.log10(Es[iE1]) * kE1 + np.log10(Es[iE2]) * kE2) + MCs = MCE / Eth + + FRBparams = [MCz, MCDM, MCb, j, MCs] + return FRBparams, pwb + def build_sz(self): pass - def update(self, vparams:dict, ALL=False, prev_grid=None): + def update(self, vparams: dict, ALL=False, prev_grid=None): """Update the grid based on a set of input parameters @@ -615,7 +669,7 @@ def update(self, vparams:dict, ALL=False, prev_grid=None): new_sfr_smear, new_pdv_smear, calc_thresh = False, False, False # Cosmology -- Only H0 so far - if self.chk_upd_param('H0', vparams, update=True): + if self.chk_upd_param("H0", vparams, update=True): reset_cos = True get_zdm = True calc_dV = True @@ -626,43 +680,51 @@ def update(self, vparams:dict, ALL=False, prev_grid=None): new_sfr_smear = True # IGM - if self.chk_upd_param('F', vparams, update=True): + if self.chk_upd_param("F", vparams, update=True): get_zdm = True smear_dm = True - calc_thresh = True - calc_pdv = True - set_evol = True + calc_thresh = False # JMB + calc_pdv = False # JMB + set_evol = False # JMB new_sfr_smear = True # DM_host # IT IS IMPORTANT TO USE np.any so that each item is executed!! - if np.any([self.chk_upd_param('lmean', vparams, update=True), - self.chk_upd_param('lsigma', vparams, update=True)]): + if np.any( + [ + self.chk_upd_param("lmean", vparams, update=True), + self.chk_upd_param("lsigma", vparams, update=True), + ] + ): smear_mask = True smear_dm = True - new_sfr_smear=True + new_sfr_smear = True # SFR? - if self.chk_upd_param('sfr_n', vparams, update=True): + if self.chk_upd_param("sfr_n", vparams, update=True): set_evol = True - new_sfr_smear=True # True for either alpha_method - if self.chk_upd_param('alpha', vparams, update=True): + new_sfr_smear = True # True for either alpha_method + if self.chk_upd_param("alpha", vparams, update=True): set_evol = True if self.state.FRBdemo.alpha_method == 0: calc_thresh = True calc_pdv = True - new_pdv_smear=True + new_pdv_smear = True elif self.state.FRBdemo.alpha_method == 1: - new_sfr_smear=True + new_sfr_smear = True ##### examines the 'pdv tree' affecting sensitivity ##### # begin with alpha # alpha does not change thresholds under rate scaling, only spec index - if np.any([self.chk_upd_param('lEmin', vparams, update=True), - self.chk_upd_param('lEmax', vparams, update=True), - self.chk_upd_param('gamma', vparams, update=True)]): + if np.any( + [ + self.chk_upd_param("lEmin", vparams, update=True), + self.chk_upd_param("lEmax", vparams, update=True), + self.chk_upd_param("gamma", vparams, update=True), + ] + ): calc_pdv = True - new_pdv_smear=True + new_pdv_smear = True # ########################### # NOW DO THE REAL WORK!! @@ -676,16 +738,26 @@ def update(self, vparams:dict, ALL=False, prev_grid=None): if get_zdm or ALL: if prev_grid is None: - zDMgrid, zvals,dmvals=misc_functions.get_zdm_grid( - self.state, new=True,plot=False,method='analytic', - save=False,nz=self.zvals.size,zmax=self.zvals[-1], - ndm=self.dmvals.size,dmmax=self.dmvals[-1],zlog=self.zlog) - self.parse_grid(zDMgrid,zvals,dmvals) + zDMgrid, zvals, dmvals = misc_functions.get_zdm_grid( + self.state, + new=True, + plot=False, + method="analytic", + save=False, + nz=self.zvals.size, + zmax=self.zvals[-1], + ndm=self.dmvals.size, + dmmax=self.dmvals[-1], + zlog=self.zlog, + ) + self.parse_grid(zDMgrid, zvals, dmvals) else: # Pass a copy (just to be safe) - self.parse_grid(prev_grid.grid.copy(), - prev_grid.zvals.copy(), - prev_grid.dmvals.copy()) + self.parse_grid( + prev_grid.grid.copy(), + prev_grid.zvals.copy(), + prev_grid.dmvals.copy(), + ) if calc_dV or ALL: if prev_grid is None: @@ -696,9 +768,11 @@ def update(self, vparams:dict, ALL=False, prev_grid=None): # Smear? if smear_mask or ALL: if prev_grid is None: - self.smear=pcosmic.get_dm_mask( - self.dmvals,(self.state.host.lmean, - self.state.host.lsigma), self.zvals) + self.smear = pcosmic.get_dm_mask( + self.dmvals, + (self.state.host.lmean, self.state.host.lsigma), + self.zvals, + ) else: self.smear = prev_grid.smear.copy() if smear_dm or ALL: @@ -707,27 +781,30 @@ def update(self, vparams:dict, ALL=False, prev_grid=None): else: self.smear = prev_grid.smear.copy() self.smear_grid = prev_grid.smear_grid.copy() - + if calc_thresh or ALL: self.calc_thresholds( - self.F0,self.eff_table, bandwidth=self.bandwidth, - weights=self.eff_weights) - + self.F0, + self.eff_table, + bandwidth=self.bandwidth, + weights=self.eff_weights, + ) + if calc_pdv or ALL: self.calc_pdv() if set_evol or ALL: - self.set_evolution() # sets star-formation rate scaling with z - here, no evoltion... + self.set_evolution() # sets star-formation rate scaling with z - here, no evoltion... if new_sfr_smear or ALL: - self.calc_rates() #includes sfr smearing factors and pdv mult + self.calc_rates() # includes sfr smearing factors and pdv mult elif new_pdv_smear: - self.rates=self.pdv*self.sfr_smear #does pdv mult only, 'by hand' + self.rates = self.pdv * self.sfr_smear # does pdv mult only, 'by hand' # Catch all the changes just in case, e.g. lC self.state.update_params(vparams) - def chk_upd_param(self, param:str, vparams:dict, update=False): + def chk_upd_param(self, param: str, vparams: dict, update=False): """ Check to see whether a parameter is differs from that in self.state From 07e42740be11d0be0f9388026199b03e80116194 Mon Sep 17 00:00:00 2001 From: Jay-MBP Date: Wed, 20 Jul 2022 10:56:13 -0700 Subject: [PATCH 023/104] make minicube batch file for slurm --- papers/F/Analysis/CRACO/Cloud/run.sh | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) create mode 100644 papers/F/Analysis/CRACO/Cloud/run.sh diff --git a/papers/F/Analysis/CRACO/Cloud/run.sh b/papers/F/Analysis/CRACO/Cloud/run.sh new file mode 100644 index 00000000..6b3683da --- /dev/null +++ b/papers/F/Analysis/CRACO/Cloud/run.sh @@ -0,0 +1,16 @@ +#!/bin/bash + +#SBATCH --job-name=craco_mini # Job name +#SBATCH --partition=cpuq # queue for job submission +#SBATCH --account=cpuq # queue for job submission +#SBATCH --mail-type=ALL +#SBATCH --mail-user=jmbaptis@ucsc.edu +#SBATCH --nodes=1 +#SBATCH --ntasks=1 +#SBATCH --ntasks-per-node=1 +#SBATCH --time=24:00:00 +#SBATCH --output=craco_mini_%j.log + +module load python/3.8.6 + +python3.8 run_craco_mini.py -n 25 -t 25 -b 1 \ No newline at end of file From 0d7839675e18b72520dbced767e5627a972e38ca Mon Sep 17 00:00:00 2001 From: profxj Date: Fri, 22 Jul 2022 11:35:35 -0700 Subject: [PATCH 024/104] yaml --- .../CRACO/Cloud/nautilus_craco_mini.yaml | 80 +++++++++++++++++++ 1 file changed, 80 insertions(+) create mode 100644 papers/F/Analysis/CRACO/Cloud/nautilus_craco_mini.yaml diff --git a/papers/F/Analysis/CRACO/Cloud/nautilus_craco_mini.yaml b/papers/F/Analysis/CRACO/Cloud/nautilus_craco_mini.yaml new file mode 100644 index 00000000..bbd22a88 --- /dev/null +++ b/papers/F/Analysis/CRACO/Cloud/nautilus_craco_mini.yaml @@ -0,0 +1,80 @@ +# 25 processors on mini for Varying F +# kubectl exec -it test-pod -- /bin/bash +apiVersion: batch/v1 +kind: Job +metadata: + name: xavier-zdm-craco-full-3rd-10 +spec: + backoffLimit: 0 + template: + spec: + affinity: + nodeAffinity: + requiredDuringSchedulingIgnoredDuringExecution: + nodeSelectorTerms: + - matchExpressions: + - key: kubernetes.io/hostname + operator: NotIn + values: + - k8s-chase-ci-01.noc.ucsb.edu + - key: nvidia.com/gpu.product + operator: In + values: + - NVIDIA-GeForce-GTX-1080-Ti + containers: + - name: container + image: localhost:30081/profxj/zdm_docker:latest # UPDATE + imagePullPolicy: Always + resources: + requests: + cpu: "25" + memory: "8Gi" # + ephemeral-storage: 50Gi # + limits: + cpu: "27" + memory: "12Gi" + ephemeral-storage: 100Gi + #nvidia.com/gpu: "1" # See docs to exlude certain types + command: ["/bin/bash", "-c"] + args: + - cd FRB/FRB; + git fetch; + git pull; + python setup.py develop; + cd ../ne2001; + python setup.py develop; + cd ../zdm; + git fetch; + git checkout varying_F; + python setup.py develop; + cd papers/F/Analysis/CRACO/Cloud; + python run_craco_full.py -n 25 -t 25 -b 1; + aws --endpoint http://rook-ceph-rgw-nautiluss3.rook s3 cp Output s3://zdm/Cubes/F/mini/ --recursive --force; + env: + - name: "ENDPOINT_URL" + value: "http://rook-ceph-rgw-nautiluss3.rook" + - name: "S3_ENDPOINT" + value: "rook-ceph-rgw-nautiluss3.rook" + volumeMounts: + - name: prp-s3-credentials + mountPath: "/root/.aws/credentials" + subPath: "credentials" + - name: ephemeral + mountPath: "/tmp" + - name: "dshm" + mountPath: "/dev/shm" + nodeSelector: + nautilus.io/disktype: nvme + restartPolicy: Never + volumes: + # Secrets file for nautilus s3 credentials .aws/credentials and .s3cfg + - name: prp-s3-credentials + secret: + secretName: prp-s3-credentials + # Shared memory (necessary for Python's multiprocessing.shared_memory module to work) + - name: dshm + emptyDir: + medium: Memory + # Ephemeral storage + - name: ephemeral + emptyDir: {} From c117b8b68a49ec9a1d6a393e9017dffba54c5836 Mon Sep 17 00:00:00 2001 From: profxj Date: Fri, 22 Jul 2022 11:48:12 -0700 Subject: [PATCH 025/104] yaml fixes --- papers/F/Analysis/CRACO/Cloud/nautilus_craco_mini.yaml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/papers/F/Analysis/CRACO/Cloud/nautilus_craco_mini.yaml b/papers/F/Analysis/CRACO/Cloud/nautilus_craco_mini.yaml index bbd22a88..3d256586 100644 --- a/papers/F/Analysis/CRACO/Cloud/nautilus_craco_mini.yaml +++ b/papers/F/Analysis/CRACO/Cloud/nautilus_craco_mini.yaml @@ -3,7 +3,7 @@ apiVersion: batch/v1 kind: Job metadata: - name: xavier-zdm-craco-full-3rd-10 + name: xavier-zdm-craco-mini-f spec: backoffLimit: 0 template: @@ -37,7 +37,7 @@ spec: #nvidia.com/gpu: "1" # See docs to exlude certain types command: ["/bin/bash", "-c"] args: - - cd FRB/FRB; + - cd FRB; git fetch; git pull; python setup.py develop; @@ -48,7 +48,7 @@ spec: git checkout varying_F; python setup.py develop; cd papers/F/Analysis/CRACO/Cloud; - python run_craco_full.py -n 25 -t 25 -b 1; + python run_craco_mini.py -n 25 -t 25 -b 1; aws --endpoint http://rook-ceph-rgw-nautiluss3.rook s3 cp Output s3://zdm/Cubes/F/mini/ --recursive --force; env: - name: "ENDPOINT_URL" From e895517fbc3a7b843d74796fd41a67b999357560 Mon Sep 17 00:00:00 2001 From: profxj Date: Fri, 22 Jul 2022 11:50:25 -0700 Subject: [PATCH 026/104] embed --- papers/F/Analysis/CRACO/Cloud/run_craco_mini.py | 1 - 1 file changed, 1 deletion(-) diff --git a/papers/F/Analysis/CRACO/Cloud/run_craco_mini.py b/papers/F/Analysis/CRACO/Cloud/run_craco_mini.py index 07eff00c..56bdf071 100644 --- a/papers/F/Analysis/CRACO/Cloud/run_craco_mini.py +++ b/papers/F/Analysis/CRACO/Cloud/run_craco_mini.py @@ -81,7 +81,6 @@ def main( # Launch em! processes = [] - embed(header='84 of run craco') for command in commands: # Popen print(f"Running this command: {' '.join(command)}") From f3b6a04cb6ac877f6b66ca6e2045875555599f18 Mon Sep 17 00:00:00 2001 From: jmbaptis Date: Fri, 22 Jul 2022 16:19:29 -0700 Subject: [PATCH 027/104] resolve conflict --- papers/F/Analysis/CRACO/Cloud/run_craco_mini.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/papers/F/Analysis/CRACO/Cloud/run_craco_mini.py b/papers/F/Analysis/CRACO/Cloud/run_craco_mini.py index 07eff00c..0239dbb4 100644 --- a/papers/F/Analysis/CRACO/Cloud/run_craco_mini.py +++ b/papers/F/Analysis/CRACO/Cloud/run_craco_mini.py @@ -81,7 +81,7 @@ def main( # Launch em! processes = [] - embed(header='84 of run craco') + for command in commands: # Popen print(f"Running this command: {' '.join(command)}") From fb1b453f87570dc25665ba82003541e8bf9febcd Mon Sep 17 00:00:00 2001 From: Jay-MBP Date: Sun, 24 Jul 2022 19:30:25 -0700 Subject: [PATCH 028/104] add new 2D cube (really a "square") for H0vF --- .../F/Analysis/CRACO/Cloud/run_craco_H0_F.py | 131 ++++++++++++++++++ .../Analysis/CRACO/Cubes/craco_H0_F_cube.json | 38 +++++ papers/F/Analysis/CRACO/py/cube_test.ipynb | 107 ++++++++++++++ 3 files changed, 276 insertions(+) create mode 100644 papers/F/Analysis/CRACO/Cloud/run_craco_H0_F.py create mode 100644 papers/F/Analysis/CRACO/Cubes/craco_H0_F_cube.json create mode 100644 papers/F/Analysis/CRACO/py/cube_test.ipynb diff --git a/papers/F/Analysis/CRACO/Cloud/run_craco_H0_F.py b/papers/F/Analysis/CRACO/Cloud/run_craco_H0_F.py new file mode 100644 index 00000000..7531f0af --- /dev/null +++ b/papers/F/Analysis/CRACO/Cloud/run_craco_H0_F.py @@ -0,0 +1,131 @@ +""" Run a Nautilus test """ + +# It should be possible to remove all the matplotlib calls from this +# but in the current implementation it is not removed. +import argparse +import numpy as np +import os, sys +from pkg_resources import resource_filename + +from concurrent.futures import ProcessPoolExecutor +import subprocess + +from zdm import iteration as it +from zdm import io + +from IPython import embed + + +def main( + pargs, + pfile: str, + oproot: str, + NFRB: int = None, + iFRB: int = 0, + outdir: str = "Output", +): + + # Generate the folder? + if not os.path.isdir(outdir): + os.mkdir(outdir) + + ############## Load up ############## + input_dict = io.process_jfile(pfile) + + # Deconstruct the input_dict + state_dict, cube_dict, vparam_dict = it.parse_input_dict(input_dict) + + npoints = np.array([item["n"] for key, item in vparam_dict.items()]) + ntotal = int(np.prod(np.abs(npoints))) + + # Total number of CPUs to be running on this Cube + total_ncpu = pargs.ncpu if pargs.total_ncpu is None else pargs.total_ncpu + batch = 1 if pargs.batch is None else pargs.batch + + nper_cpu = ntotal // total_ncpu + if int(ntotal / total_ncpu) != nper_cpu: + raise IOError(f"Ncpu={total_ncpu} must divide evenly into ntotal={ntotal}") + + survey_file = os.path.join( + resource_filename("zdm", "craco"), "MC_F", "Surveys", "F_0.32_survey" + ) + commands = [] + for kk in range(pargs.ncpu): + line = [] + # Which CPU is running out of the total? + iCPU = (batch - 1) * pargs.ncpu + kk + outfile = os.path.join(outdir, oproot.replace(".csv", f"{iCPU+1}.csv")) + # Command + line = [ + "zdm_build_cube", + "-n", + f"{iCPU+1}", + "-m", + f"{nper_cpu}", + "-o", + f"{outfile}", + "-s", + f"{survey_file}", + "--clobber", + "-p", + f"{pfile}", + ] + # NFRB? + if NFRB is not None: + line += [f"--NFRB", f"{NFRB}"] + # iFRB? + if iFRB > 0: + line += [f"--iFRB", f"{iFRB}"] + # Finish + # line += ' & \n' + commands.append(line) + + # Launch em! + processes = [] + + for command in commands: + # Popen + print(f"Running this command: {' '.join(command)}") + pw = subprocess.Popen(command) + processes.append(pw) + + # Wait on em! + for pw in processes: + pw.wait() + + print("All done!") + + +def parse_option(): + # test for command-line arguments here + parser = argparse.ArgumentParser() + parser.add_argument( + "-n", + "--ncpu", + type=int, + required=True, + help="Number of CPUs to run on (might be split in batches)", + ) + parser.add_argument( + "-t", + "--total_ncpu", + type=int, + required=False, + help="Total number of CPUs to run on (might be split in batches)", + ) + parser.add_argument( + "-b", "--batch", type=int, default=1, required=False, help="Batch number" + ) + # parser.add_argument('--NFRB',type=int,required=False,help="Number of FRBs to analzye") + # parser.add_argument('--iFRB',type=int,default=0,help="Initial FRB to run from") + args = parser.parse_args() + + return args + + +if __name__ == "__main__": + # get the argument of training. + pfile = "../Cubes/craco_H0_F_cube.json" + oproot = "craco_H0_F.csv" + pargs = parse_option() + main(pargs, pfile, oproot, NFRB=100, iFRB=100) diff --git a/papers/F/Analysis/CRACO/Cubes/craco_H0_F_cube.json b/papers/F/Analysis/CRACO/Cubes/craco_H0_F_cube.json new file mode 100644 index 00000000..71fbe7d0 --- /dev/null +++ b/papers/F/Analysis/CRACO/Cubes/craco_H0_F_cube.json @@ -0,0 +1,38 @@ +{ + "state": { + "energy": { + "luminosity_function": 1 + }, + "FRBdemo": { + "alpha_method": 1 + }, + "cosmo": { + "fix_Omega_b_h2": true + } + }, + "cube": { + "parameter_order": [ + "lC", + "F", + "H0" + ] + }, + "F": { + "DC": "IGM", + "min": 0.01, + "max": 1.0, + "n": 50 + }, + "H0": { + "DC": "cosmo", + "min": 55.0, + "max": 80.0, + "n": 50 + }, + "lC": { + "DC": "FRBdemo", + "min": -0.911, + "max": -0.911, + "n": -1 + } +} \ No newline at end of file diff --git a/papers/F/Analysis/CRACO/py/cube_test.ipynb b/papers/F/Analysis/CRACO/py/cube_test.ipynb new file mode 100644 index 00000000..76acc3db --- /dev/null +++ b/papers/F/Analysis/CRACO/py/cube_test.ipynb @@ -0,0 +1,107 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "cube = np.load(\"../Cubes/craco_mini_cube.npz\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(10, 25, 3, 5, 20, 5, 5, 15)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ll = cube[\"ll\"]\n", + "ll.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "F = cube[\"F\"]\n", + "H0 = cube[\"H0\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "dF = F[1]-F[0]\n", + "dH = H0[1] - H0[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "ll[np.isnan(ll)]=-1e99" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.8.5 ('base')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "9731a3b7ebb41545a410367c249afbc4842fd5740da82f1bd29e6ac1d7253e6e" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 1da999f85e7a2842fc45cc3dd5608bc94bc6af3d Mon Sep 17 00:00:00 2001 From: Jay-MBP Date: Sun, 24 Jul 2022 20:14:12 -0700 Subject: [PATCH 029/104] fix lum function in H0vF cube --- papers/F/Analysis/CRACO/Cubes/craco_H0_F_cube.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/papers/F/Analysis/CRACO/Cubes/craco_H0_F_cube.json b/papers/F/Analysis/CRACO/Cubes/craco_H0_F_cube.json index 71fbe7d0..5ffbb63c 100644 --- a/papers/F/Analysis/CRACO/Cubes/craco_H0_F_cube.json +++ b/papers/F/Analysis/CRACO/Cubes/craco_H0_F_cube.json @@ -1,7 +1,7 @@ { "state": { "energy": { - "luminosity_function": 1 + "luminosity_function": 2 }, "FRBdemo": { "alpha_method": 1 From 4187eb5f7d2819fa84d3fd75267ce141a3edbfc3 Mon Sep 17 00:00:00 2001 From: profxj Date: Tue, 26 Jul 2022 15:33:57 -0700 Subject: [PATCH 030/104] rm False --- zdm/grid.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/zdm/grid.py b/zdm/grid.py index 85db06ad..9bc161b3 100644 --- a/zdm/grid.py +++ b/zdm/grid.py @@ -683,9 +683,9 @@ def update(self, vparams: dict, ALL=False, prev_grid=None): if self.chk_upd_param("F", vparams, update=True): get_zdm = True smear_dm = True - calc_thresh = False # JMB - calc_pdv = False # JMB - set_evol = False # JMB + #calc_thresh = False # JMB + #calc_pdv = False # JMB + #set_evol = False # JMB new_sfr_smear = True # DM_host From 28c1216ba2c7baa8130b7a1fbad6ea5ea7b35d36 Mon Sep 17 00:00:00 2001 From: Jay-MBP Date: Tue, 26 Jul 2022 18:17:03 -0700 Subject: [PATCH 031/104] add new H0_F cube --- .../Analysis/CRACO/Cubes/craco_H0_F_cube.json | 38 +++++++++++++++++++ .../Analysis/CRACO/Cubes/craco_mini_cube.json | 2 +- .../F/Analysis/CRACO/py/craco_qck_explore.py | 3 ++ .../F/Analysis/CRACO/py/slurp_craco_cubes.py | 2 +- zdm/grid.py | 6 +-- 5 files changed, 46 insertions(+), 5 deletions(-) create mode 100644 papers/F/Analysis/CRACO/Cubes/craco_H0_F_cube.json diff --git a/papers/F/Analysis/CRACO/Cubes/craco_H0_F_cube.json b/papers/F/Analysis/CRACO/Cubes/craco_H0_F_cube.json new file mode 100644 index 00000000..5ffbb63c --- /dev/null +++ b/papers/F/Analysis/CRACO/Cubes/craco_H0_F_cube.json @@ -0,0 +1,38 @@ +{ + "state": { + "energy": { + "luminosity_function": 2 + }, + "FRBdemo": { + "alpha_method": 1 + }, + "cosmo": { + "fix_Omega_b_h2": true + } + }, + "cube": { + "parameter_order": [ + "lC", + "F", + "H0" + ] + }, + "F": { + "DC": "IGM", + "min": 0.01, + "max": 1.0, + "n": 50 + }, + "H0": { + "DC": "cosmo", + "min": 55.0, + "max": 80.0, + "n": 50 + }, + "lC": { + "DC": "FRBdemo", + "min": -0.911, + "max": -0.911, + "n": -1 + } +} \ No newline at end of file diff --git a/papers/F/Analysis/CRACO/Cubes/craco_mini_cube.json b/papers/F/Analysis/CRACO/Cubes/craco_mini_cube.json index 71384436..cedf3118 100644 --- a/papers/F/Analysis/CRACO/Cubes/craco_mini_cube.json +++ b/papers/F/Analysis/CRACO/Cubes/craco_mini_cube.json @@ -69,7 +69,7 @@ "DC": "IGM", "min": 0.01, "max": 0.99, - "n": 15 + "n": 20 }, "lC": { "DC": "FRBdemo", diff --git a/papers/F/Analysis/CRACO/py/craco_qck_explore.py b/papers/F/Analysis/CRACO/py/craco_qck_explore.py index cd4169f2..18be27d0 100644 --- a/papers/F/Analysis/CRACO/py/craco_qck_explore.py +++ b/papers/F/Analysis/CRACO/py/craco_qck_explore.py @@ -19,6 +19,9 @@ def main(pargs): if pargs.run == "mini": scube = "mini" outdir = "Mini/" + elif pargs.run == "F": + scube = "H0_F" + outdir = "H0_F/" elif pargs.run == "full": scube = "full" outdir = "Full/" diff --git a/papers/F/Analysis/CRACO/py/slurp_craco_cubes.py b/papers/F/Analysis/CRACO/py/slurp_craco_cubes.py index e8d6087a..983a2ded 100644 --- a/papers/F/Analysis/CRACO/py/slurp_craco_cubes.py +++ b/papers/F/Analysis/CRACO/py/slurp_craco_cubes.py @@ -19,7 +19,7 @@ def main(pargs): elif pargs.run == "F": # Emax input_file = "Cubes/craco_H0_F_cube.json" - prefix = "Cubes/craco_H0_F_cube" + prefix = "Cloud/OutputH0F/craco_H0_F" nsurveys = 1 # Run it diff --git a/zdm/grid.py b/zdm/grid.py index 85db06ad..8a280e3f 100644 --- a/zdm/grid.py +++ b/zdm/grid.py @@ -683,9 +683,9 @@ def update(self, vparams: dict, ALL=False, prev_grid=None): if self.chk_upd_param("F", vparams, update=True): get_zdm = True smear_dm = True - calc_thresh = False # JMB - calc_pdv = False # JMB - set_evol = False # JMB + # calc_thresh = False # JMB + # calc_pdv = False # JMB + # set_evol = False # JMB new_sfr_smear = True # DM_host From d1169bf07c05f2dd74834182f6eb58670d2b9861 Mon Sep 17 00:00:00 2001 From: profxj Date: Wed, 27 Jul 2022 07:45:28 -0700 Subject: [PATCH 032/104] more exploring --- papers/F/Analysis/py/analy_F_I.py | 4 ++-- papers/F/Figures/py/figs_zdm_F_I.py | 20 +++++++++++++++++--- zdm/craco/testing.py | 10 ++++++++++ 3 files changed, 29 insertions(+), 5 deletions(-) diff --git a/papers/F/Analysis/py/analy_F_I.py b/papers/F/Analysis/py/analy_F_I.py index 097c084c..12a8e289 100644 --- a/papers/F/Analysis/py/analy_F_I.py +++ b/papers/F/Analysis/py/analy_F_I.py @@ -3,9 +3,9 @@ fiducial_survey = "CRACO_std_May2022" -def craco_mc_survey_grid(): +def craco_mc_survey_grid(iFRB=100): """ Load the defaul MonteCarlo survey+grid for CRACO """ survey, grid = loading.survey_and_grid( - survey_name=fiducial_survey, NFRB=100, lum_func=2, iFRB=100 + survey_name=fiducial_survey, NFRB=100, lum_func=2, iFRB=iFRB ) return survey, grid diff --git a/papers/F/Figures/py/figs_zdm_F_I.py b/papers/F/Figures/py/figs_zdm_F_I.py index 771175b2..d9b95a43 100644 --- a/papers/F/Figures/py/figs_zdm_F_I.py +++ b/papers/F/Figures/py/figs_zdm_F_I.py @@ -329,6 +329,8 @@ def fig_craco_fiducial_F( grid=None, survey=None, F=0.03, + H0=None, + iFRB=100, suppress_DM_host=False, ): """ @@ -355,12 +357,15 @@ def fig_craco_fiducial_F( """ # Generate the grid if grid is None or survey is None: - survey, grid = analy_F_I.craco_mc_survey_grid() + survey, grid = analy_F_I.craco_mc_survey_grid(iFRB=iFRB) fiducial_H0 = grid.state.cosmo.H0 vparams = {"H0": fiducial_H0, "F": F} + if H0 is not None: + vparams['H0'] = H0 + if suppress_DM_host: # Sets the log-normal distribution for DM_host to ~0. vparams["lmean"] = 1e-3 @@ -503,7 +508,8 @@ def fig_craco_fiducial_F( # fig_craco_fiducial_F("fig_craco_fiducial_F_0.01.png", show_Macquart=True, F=0.01, suppress_DM_host=True) # fig_craco_fiducial_F("fig_craco_fiducial_F_0.9.png", show_Macquart=True, F=0.9, suppress_DM_host=True) -# fig_craco_fiducial_F("fig_craco_fiducial_dmhost_F_0.32.png", show_Macquart=True, F=0.32, suppress_DM_host=False) +#fig_craco_fiducial_F("fig_craco_fiducial_dmhost_F_0.32.png", show_Macquart=False, F=0.32, suppress_DM_host=False) +#fig_craco_fiducial_F("fig_craco_fiducial_dmhost_F_0.82_H0_55.png", show_Macquart=False, F=0.82, H0=55., suppress_DM_host=False) # fig_craco_fiducial_F("fig_craco_fiducial_dmhost_F_0.01.png", show_Macquart=True, F=0.01, suppress_DM_host=False) # fig_craco_fiducial_F("fig_craco_fiducial_dmhost_F_0.9.png", show_Macquart=True, F=0.9, suppress_DM_host=False) @@ -539,4 +545,12 @@ def fig_craco_fiducial_F( # DMmax=1800, # ) -fig_craco_varyF_zDM("strawberry.png", other_param="lmean") +#fig_craco_varyF_zDM("strawberry.png", other_param="lmean") + +# Fussing on the square +#fig_craco_fiducial_F("fig_craco_fiducial_dmhost_F_0.32.png", show_Macquart=False, F=0.32, suppress_DM_host=False) +#fig_craco_fiducial_F("fig_craco_fiducial_dmhost_F_0.82_H0_55.png", show_Macquart=False, F=0.82, H0=55., suppress_DM_host=False) + +# iFRB = 0 +#fig_craco_fiducial_F("fig_craco_fiducial_dmhost_F_0.99_H0_55_i0.png", show_Macquart=False, F=0.99, H0=55., suppress_DM_host=False, iFRB=0) +fig_craco_fiducial_F("fig_craco_fiducial_dmhost_F_0.32_i0.png", show_Macquart=False, F=0.32, suppress_DM_host=False, iFRB=0) \ No newline at end of file diff --git a/zdm/craco/testing.py b/zdm/craco/testing.py index 2d143681..f1d4f767 100644 --- a/zdm/craco/testing.py +++ b/zdm/craco/testing.py @@ -52,6 +52,9 @@ def main(pargs): vparams[pargs.param] = None vparams["lC"] = -0.9 + # JXP Fussing + vparams["H0"] = 55. + ''' tparams = pandas.read_csv('tst_params.csv') for key in ['lEmax', 'alpha','gamma','sfr_n','lmean','lsigma','F']: @@ -220,5 +223,12 @@ def main(pargs): # More fussing about with F and related python testing.py H0 60. 80. --nstep 50 --nFRB 100 -o MC_Plots/CRACO_100_H0_TEST_F32.png --lum_func 2 --survey ../MC_F/Surveys/F_0.32_survey +# Square debugging +python testing.py F 0.1 0.99 --nstep 50 --nFRB 100 -o MC_Plots/CRACO_100_F_TEST_F32.png --lum_func 2 --survey ../MC_F/Surveys/F_0.32_survey --iFRB 100 + # Best F: pval=0.2997959183673469, C=3.630489354871595, lltot=-565.4650145414604 +python testing.py F 0.1 0.99 --nstep 50 --nFRB 100 -o MC_Plots/CRACO_100_F_TEST_F32_H055.png --lum_func 2 --survey ../MC_F/Surveys/F_0.32_survey --iFRB 100 + # Best F: pval=0.8265306122448979, C=3.6174093413949553, lltot=-567.7777429522436 + + """ From b7cdf381ac31d97d639cd6850a39d579a53c4e5c Mon Sep 17 00:00:00 2001 From: profxj Date: Wed, 27 Jul 2022 07:45:46 -0700 Subject: [PATCH 033/104] H0 --- zdm/craco/testing.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/zdm/craco/testing.py b/zdm/craco/testing.py index f1d4f767..c74c9705 100644 --- a/zdm/craco/testing.py +++ b/zdm/craco/testing.py @@ -53,7 +53,7 @@ def main(pargs): vparams["lC"] = -0.9 # JXP Fussing - vparams["H0"] = 55. + #vparams["H0"] = 55. ''' tparams = pandas.read_csv('tst_params.csv') From cbc97a8db5567df731a0631b58135f9b63af1fb9 Mon Sep 17 00:00:00 2001 From: profxj Date: Wed, 27 Jul 2022 09:58:36 -0700 Subject: [PATCH 034/104] fussin about --- papers/F/Analysis/CRACO/py/slurp_craco_cubes.py | 2 ++ papers/H0_I/Figures/H0_vs_Emax.ipynb | 4 ++-- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/papers/F/Analysis/CRACO/py/slurp_craco_cubes.py b/papers/F/Analysis/CRACO/py/slurp_craco_cubes.py index 983a2ded..0ea0e0e1 100644 --- a/papers/F/Analysis/CRACO/py/slurp_craco_cubes.py +++ b/papers/F/Analysis/CRACO/py/slurp_craco_cubes.py @@ -127,3 +127,5 @@ def parse_option(): # python py/slurp_craco_cubes.py mini # python py/slurp_craco_cubes.py another_full + +# python py/slurp_craco_cubes.py F \ No newline at end of file diff --git a/papers/H0_I/Figures/H0_vs_Emax.ipynb b/papers/H0_I/Figures/H0_vs_Emax.ipynb index 0987c14d..2b8f1543 100644 --- a/papers/H0_I/Figures/H0_vs_Emax.ipynb +++ b/papers/H0_I/Figures/H0_vs_Emax.ipynb @@ -245,7 +245,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -259,7 +259,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.6" + "version": "3.9.9" } }, "nbformat": 4, From 39c897055abb9a8746b5fdd945d63aa335d5854b Mon Sep 17 00:00:00 2001 From: profxj Date: Wed, 27 Jul 2022 09:59:50 -0700 Subject: [PATCH 035/104] nb --- papers/F/Analysis/CRACO/Fussing_on_F_H0.ipynb | 1428 +++++++++++++++++ 1 file changed, 1428 insertions(+) create mode 100644 papers/F/Analysis/CRACO/Fussing_on_F_H0.ipynb diff --git a/papers/F/Analysis/CRACO/Fussing_on_F_H0.ipynb b/papers/F/Analysis/CRACO/Fussing_on_F_H0.ipynb new file mode 100644 index 00000000..a44f3e5e --- /dev/null +++ b/papers/F/Analysis/CRACO/Fussing_on_F_H0.ipynb @@ -0,0 +1,1428 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "8e9d01fb-000b-4558-80e8-27688eafa19e", + "metadata": {}, + "source": [ + "# Quick check" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "7a372b56-1bb5-40be-bdf8-4129f926399e", + "metadata": {}, + "outputs": [], + "source": [ + "# imports\n", + "import numpy as np\n", + "import pandas\n", + "\n", + "import seaborn as sns\n", + "\n", + "from matplotlib import pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "id": "d5e74992-02f4-4cf5-a9af-6672bb8d5b4d", + "metadata": {}, + "source": [ + "# Read one" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "4df320dd-109c-4c74-bc35-760d3d4f07ee", + "metadata": {}, + "outputs": [], + "source": [ + "df_1 = pandas.read_csv('Cloud/Output/craco_H0_F1.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "1a86735b-ab9e-464a-a4a1-576700653452", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = sns.scatterplot(data=df_comb, x='F', y='lls', hue='H0')\n", + "\n", + "xlim = [0.1, 1.]\n", + "ax.set_xlim(xlim)\n", + "ax.set_ylim(-600., -550.)\n", + "\n", + "# Max line\n", + "max_LL = df_comb.lls.max()\n", + "ax.plot(xlim, [max_LL]*2, 'g--')" + ] + }, + { + "cell_type": "markdown", + "id": "b5df69af-6901-40c2-beba-0212182bdd16", + "metadata": {}, + "source": [ + "# I am suspecting a slurp bug.." + ] + }, + { + "cell_type": "markdown", + "id": "f5438dc7-61d6-4a67-9aaa-3248fbdc4a5b", + "metadata": {}, + "source": [ + "# I slurped, now am examining the slurped file" + ] + }, + { + "cell_type": "markdown", + "id": "8ef3b730-fea3-40aa-9b7d-34814e9c2024", + "metadata": {}, + "source": [ + "## Load" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "654b497a-e590-4762-a0d2-4ce1c84306dc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['ll',\n", + " 'lC',\n", + " 'params',\n", + " 'pzDM',\n", + " 'pDM',\n", + " 'pDMz',\n", + " 'pz',\n", + " 'F',\n", + " 'H0',\n", + " 'lls0',\n", + " 'P_zDM0',\n", + " 'P_n0',\n", + " 'P_s0',\n", + " 'N0']" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cube = np.load('Cubes/craco_H0_F_cube.npz')\n", + "list(cube.keys())" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "5a1f38c3-2276-4db4-8b85-b562c218f260", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(50, 50)" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "LL = cube['ll']\n", + "LL.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "ffb3969c-843c-4186-8e8b-71132b959441", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "-567.777" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.nanmax(LL[:,0])" + ] + }, + { + "cell_type": "markdown", + "id": "77e3c617-d266-4a7f-940f-170549619732", + "metadata": {}, + "source": [ + "## Parse" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "b7267261-fb2f-4686-9521-559730c3b3ed", + "metadata": {}, + "outputs": [], + "source": [ + "F = cube['F']\n", + "H0 = cube['H0']\n", + "#\n", + "dF = F[1]-F[0]\n", + "dH = H0[1] - H0[0]" + ] + }, + { + "cell_type": "markdown", + "id": "59934a22-f603-4c5a-b5b1-86b4cf7b0ac8", + "metadata": {}, + "source": [ + "## Plot" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "adf1fdda-aa1c-4ee2-850d-82f36e5835c3", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.clf()\n", + "ax = plt.gca()\n", + "ax.plot(LL[:,0])\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "9b85c517-7fcc-408c-bc68-8f85639b5201", + "metadata": {}, + "source": [ + "## Show it all" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "24b700b6-1433-4a73-bde8-b5f9f9b5fd9c", + "metadata": {}, + "outputs": [], + "source": [ + "nans = np.isnan(LL)\n", + "LL_clean = LL.copy()\n", + "LL_clean[nans] = -9e9\n", + "#\n", + "LL_clean -= LL_clean.max()" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "493e0416-168e-438a-9d29-40b095dbccbf", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'H0 (km/s/Mpc)')" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.clf()\n", + "ax=plt.gca()\n", + "#\n", + "im = plt.imshow(LL_clean.T, origin='lower', vmin=-4., vmax=0., cmap='jet',\n", + " extent=[F.min()-dF/2, F.max()+dF/2, 55.-dH/2, 80+dH/2], aspect='auto')\n", + "# Color bar\n", + "cbar=plt.colorbar(im,fraction=0.046, shrink=1.2,aspect=15,pad=0.05)\n", + "cbar.set_label(r'$\\Delta$ Log10 Likelihood')\n", + "#\n", + "ax.set_xlabel('F')\n", + "ax.set_ylabel('H0 (km/s/Mpc)')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1e57d2a5-f711-4e40-bcf0-4de0576ec60a", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From b16190c676ae0091449f207bc381761f0f2c465f Mon Sep 17 00:00:00 2001 From: profxj Date: Wed, 27 Jul 2022 10:39:49 -0700 Subject: [PATCH 036/104] mini cube --- papers/F/Analysis/CRACO/py/craco_qck_explore.py | 2 +- papers/F/Analysis/CRACO/py/slurp_craco_cubes.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/papers/F/Analysis/CRACO/py/craco_qck_explore.py b/papers/F/Analysis/CRACO/py/craco_qck_explore.py index 18be27d0..509fdf64 100644 --- a/papers/F/Analysis/CRACO/py/craco_qck_explore.py +++ b/papers/F/Analysis/CRACO/py/craco_qck_explore.py @@ -91,4 +91,4 @@ def parse_option(): pargs = parse_option() main(pargs) -# python py/slurp_craco_cubes.py mini +# python py/craco_qck_explore.py mini diff --git a/papers/F/Analysis/CRACO/py/slurp_craco_cubes.py b/papers/F/Analysis/CRACO/py/slurp_craco_cubes.py index 0ea0e0e1..b13a1b10 100644 --- a/papers/F/Analysis/CRACO/py/slurp_craco_cubes.py +++ b/papers/F/Analysis/CRACO/py/slurp_craco_cubes.py @@ -30,7 +30,7 @@ def main(pargs): # Emax input_file = "Cubes/craco_mini_cube.json" # prefix = 'Cubes/craco_mini' - prefix = "Cloud/Output/craco_mini" + prefix = "Cloud/OutputMini/craco_mini" nsurveys = 1 # Run it From 8b46a6ba2d6ab2358e8e8d4e56bc9ed2bff22e8a Mon Sep 17 00:00:00 2001 From: Jay-MBP Date: Wed, 27 Jul 2022 11:30:07 -0700 Subject: [PATCH 037/104] add state file --- .../CRACO/Cubes/craco_H0_F_state.json | 57 +++++++++++++++++++ 1 file changed, 57 insertions(+) create mode 100644 papers/F/Analysis/CRACO/Cubes/craco_H0_F_state.json diff --git a/papers/F/Analysis/CRACO/Cubes/craco_H0_F_state.json b/papers/F/Analysis/CRACO/Cubes/craco_H0_F_state.json new file mode 100644 index 00000000..38ba399e --- /dev/null +++ b/papers/F/Analysis/CRACO/Cubes/craco_H0_F_state.json @@ -0,0 +1,57 @@ +{ + "FRBdemo": { + "alpha_method": 1, + "lC": 1, + "sfr_n": 0.73, + "source_evolution": 0 + }, + "IGM": { + "F": 0.32 + }, + "MW": { + "DMhalo": 50, + "ISM": 35.0 + }, + "analysis": { + "NewGrids": true, + "sprefix": "Std" + }, + "beam": { + "Bmethod": 2, + "Bthresh": 0 + }, + "cosmo": { + "H0": 67.66, + "Omega_b": 0.04897, + "Omega_b_h2": 0.0224178568132, + "Omega_k": 0.0, + "Omega_lambda": 0.6888463055445441, + "Omega_m": 0.30966, + "fix_Omega_b_h2": true + }, + "energy": { + "alpha": 0.65, + "gamma": -1.01, + "lEmax": 41.4, + "lEmin": 30, + "luminosity_function": 2 + }, + "host": { + "lmean": 2.18, + "lsigma": 0.48 + }, + "scat": { + "Sfnorm": 600, + "Sfpower": -4.0, + "Slogmean": 0.7, + "Slogsigma": 1.9 + }, + "width": { + "Wbins": 10, + "Wlogmean": 1.70267, + "Wlogsigma": 0.899148, + "Wmethod": 2, + "Wscale": 2, + "Wthresh": 0.5 + } +} \ No newline at end of file From 7852391a3e1f1dc2271739ec9cb73c722e01aba2 Mon Sep 17 00:00:00 2001 From: Jay-MBP Date: Sat, 30 Jul 2022 22:00:00 -0700 Subject: [PATCH 038/104] make 2d plots for minicube --- .../Cloud/OutputMini/nautilus_craco_mini.yaml | 80 ++++++++ papers/F/Analysis/CRACO/Fussing_on_F_H0.ipynb | 19 +- papers/F/Analysis/CRACO/marginalize.ipynb | 187 ++++++++++++++++++ 3 files changed, 279 insertions(+), 7 deletions(-) create mode 100644 papers/F/Analysis/CRACO/Cloud/OutputMini/nautilus_craco_mini.yaml create mode 100644 papers/F/Analysis/CRACO/marginalize.ipynb diff --git a/papers/F/Analysis/CRACO/Cloud/OutputMini/nautilus_craco_mini.yaml b/papers/F/Analysis/CRACO/Cloud/OutputMini/nautilus_craco_mini.yaml new file mode 100644 index 00000000..bbd22a88 --- /dev/null +++ b/papers/F/Analysis/CRACO/Cloud/OutputMini/nautilus_craco_mini.yaml @@ -0,0 +1,80 @@ +# 25 processors on mini for Varying F +# kubectl exec -it test-pod -- /bin/bash +apiVersion: batch/v1 +kind: Job +metadata: + name: xavier-zdm-craco-full-3rd-10 +spec: + backoffLimit: 0 + template: + spec: + affinity: + nodeAffinity: + requiredDuringSchedulingIgnoredDuringExecution: + nodeSelectorTerms: + - matchExpressions: + - key: kubernetes.io/hostname + operator: NotIn + values: + - k8s-chase-ci-01.noc.ucsb.edu + - key: nvidia.com/gpu.product + operator: In + values: + - NVIDIA-GeForce-GTX-1080-Ti + containers: + - name: container + image: localhost:30081/profxj/zdm_docker:latest # UPDATE + imagePullPolicy: Always + resources: + requests: + cpu: "25" + memory: "8Gi" # + ephemeral-storage: 50Gi # + limits: + cpu: "27" + memory: "12Gi" + ephemeral-storage: 100Gi + #nvidia.com/gpu: "1" # See docs to exlude certain types + command: ["/bin/bash", "-c"] + args: + - cd FRB/FRB; + git fetch; + git pull; + python setup.py develop; + cd ../ne2001; + python setup.py develop; + cd ../zdm; + git fetch; + git checkout varying_F; + python setup.py develop; + cd papers/F/Analysis/CRACO/Cloud; + python run_craco_full.py -n 25 -t 25 -b 1; + aws --endpoint http://rook-ceph-rgw-nautiluss3.rook s3 cp Output s3://zdm/Cubes/F/mini/ --recursive --force; + env: + - name: "ENDPOINT_URL" + value: "http://rook-ceph-rgw-nautiluss3.rook" + - name: "S3_ENDPOINT" + value: "rook-ceph-rgw-nautiluss3.rook" + volumeMounts: + - name: prp-s3-credentials + mountPath: "/root/.aws/credentials" + subPath: "credentials" + - name: ephemeral + mountPath: "/tmp" + - name: "dshm" + mountPath: "/dev/shm" + nodeSelector: + nautilus.io/disktype: nvme + restartPolicy: Never + volumes: + # Secrets file for nautilus s3 credentials .aws/credentials and .s3cfg + - name: prp-s3-credentials + secret: + secretName: prp-s3-credentials + # Shared memory (necessary for Python's multiprocessing.shared_memory module to work) + - name: dshm + emptyDir: + medium: Memory + # Ephemeral storage + - name: ephemeral + emptyDir: {} diff --git a/papers/F/Analysis/CRACO/Fussing_on_F_H0.ipynb b/papers/F/Analysis/CRACO/Fussing_on_F_H0.ipynb index a44f3e5e..3e56702d 100644 --- a/papers/F/Analysis/CRACO/Fussing_on_F_H0.ipynb +++ b/papers/F/Analysis/CRACO/Fussing_on_F_H0.ipynb @@ -462,7 +462,7 @@ }, { "data": { - "image/png": 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\n", 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", 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", 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\n", 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", 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", 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\n", 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", 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" ] @@ -1406,7 +1406,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3.8.5 ('base')", "language": "python", "name": "python3" }, @@ -1420,7 +1420,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.9" + "version": "3.8.5" + }, + "vscode": { + "interpreter": { + "hash": "9731a3b7ebb41545a410367c249afbc4842fd5740da82f1bd29e6ac1d7253e6e" + } } }, "nbformat": 4, diff --git a/papers/F/Analysis/CRACO/marginalize.ipynb b/papers/F/Analysis/CRACO/marginalize.ipynb new file mode 100644 index 00000000..f435112f --- /dev/null +++ b/papers/F/Analysis/CRACO/marginalize.ipynb @@ -0,0 +1,187 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import zdm.analyze_cube as ac" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "cube_dir = \"../CRACO/Cubes/craco_mini_cube.npz\"\n", + "cube_data = np.load(cube_dir)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def get_param_values(data,params):\n", + " \"\"\"\n", + " Gets the unique values of the data from a cube output\n", + " Currently the parameter order is hard-coded\n", + " \n", + " \"\"\"\n", + " # gets unique values for each axis\n", + " param_vals=[]\n", + " #param_list=[data[\"lEmax\"],data[\"H0\"],data[\"alpha\"],data[\"gamma\"],data[\"sfr_n\"],data[\"lmean\"],data[\"lsigma\"]]\n", + " #for col in param_list:\n", + " for param in params:\n", + " col=data[param]\n", + " unique=np.unique(col)\n", + " param_vals.append(unique) \n", + " return param_vals" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "ll = cube_data[\"ll\"]\n", + "params = cube_data[\"params\"]\n", + "param_vals = get_param_values(cube_data, params)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "uvals=[]\n", + "latexnames=[]\n", + "for ip,param in enumerate(cube_data[\"params\"]):\n", + " # switches for alpha\n", + " if param==\"alpha\":\n", + " uvals.append(cube_data[param]*-1.)\n", + " else:\n", + " uvals.append(cube_data[param])\n", + " if param==\"alpha\":\n", + " latexnames.append('$\\\\alpha$')\n", + " ialpha=ip\n", + " elif param==\"F\":\n", + " latexnames.append('$F$')\n", + " elif param==\"lEmax\":\n", + " latexnames.append('$\\\\log_{10} E_{\\\\rm max}$')\n", + " elif param==\"H0\":\n", + " latexnames.append('$H_0$')\n", + " elif param==\"gamma\":\n", + " latexnames.append('$\\\\gamma$')\n", + " elif param==\"sfr_n\":\n", + " latexnames.append('$n_{\\\\rm sfr}$')\n", + " elif param==\"lmean\":\n", + " latexnames.append('$\\\\mu_{\\\\rm host}$')\n", + " elif param==\"lsigma\":\n", + " latexnames.append('$\\\\sigma_{\\\\rm host}$')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "deprecated, uw_vectors, wvectors=ac.get_bayesian_data(cube_data[\"ll\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "list2 = []\n", + "vals2 = []\n", + "\n", + "for i, vec in enumerate(wvectors):\n", + " n_idx = np.argmax(vec)\n", + " val = uvals[i][n_idx]\n", + "\n", + " if params[i] != \"F\":\n", + " list2.append(params[i])\n", + " vals2.append(val)\n", + " else:\n", + " iF = i\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "for i, p in enumerate(list2):\n", + " list3 = np.concatenate((list2[0:i], list2[i+1:]))\n", + " vals3 = np.concatenate((vals2[0:i], vals2[i+1:]))\n", + " arr = ac.get_slice_from_parameters(cube_data, list3, vals3)\n", + "\n", + " nans = np.isnan(arr)\n", + " arr[nans] = -9e9\n", + "\n", + " arr -= np.max(arr)\n", + " arr = 10**arr\n", + " arr /= np.sum(arr)\n", + "\n", + " if i < iF:\n", + " modi = i\n", + " else:\n", + " modi = i+1\n", + " arr = arr.swapaxes(0,1)\n", + "\n", + " savename = f\"2D Plots/{params[iF]}_{params[modi]}.pdf\"\n", + "\n", + " if params[modi] == \"alpha\":\n", + " arr = np.flip(arr, axis=0)\n", + " ac.make_2d_plot(arr, latexnames[modi], latexnames[iF], -param_vals[modi], param_vals[iF], savename=savename, norm=1)\n", + " else:\n", + " ac.make_2d_plot(arr, latexnames[modi], latexnames[iF], param_vals[modi], param_vals[iF], savename=savename, norm=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.8.5 ('base')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "9731a3b7ebb41545a410367c249afbc4842fd5740da82f1bd29e6ac1d7253e6e" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 8828337517fe7fd69efc0faf84806beec422abf6 Mon Sep 17 00:00:00 2001 From: Jay-MBP Date: Tue, 2 Aug 2022 21:44:15 -0700 Subject: [PATCH 039/104] lower contour analysis --- papers/F/Analysis/CRACO/Contour/lower_CI.py | 184 ++++++++++++++++++++ 1 file changed, 184 insertions(+) create mode 100644 papers/F/Analysis/CRACO/Contour/lower_CI.py diff --git a/papers/F/Analysis/CRACO/Contour/lower_CI.py b/papers/F/Analysis/CRACO/Contour/lower_CI.py new file mode 100644 index 00000000..3bcb86e2 --- /dev/null +++ b/papers/F/Analysis/CRACO/Contour/lower_CI.py @@ -0,0 +1,184 @@ +from webbrowser import get +import numpy as np +import zdm +import matplotlib.pyplot as plt +from frb.dm import cosmic +from zdm.pcosmic import pcosmic, get_mean_DM +from zdm.parameters import State +import scipy.stats + +from IPython import embed + +fC0 = cosmic.grab_C0_spline() + + +def lower_ci(data, conflevel=0.95): + mu, sigma = np.mean(data), scipy.stats.sem(data) + k = sigma * scipy.stats.t.ppf((1 + conflevel) / 2.0, len(data) - 1) + return mu - k + + +def lowerCI_F( + Fs, + H0=None, + z=0.5, + deltas=np.linspace(0.01, 5, 200), + niter_per_F=1000, + ns_per_F=1000, +): + + z = np.array(z).reshape(1) + + n = len(Fs) + + lower_cis = np.zeros(n) + + state = State() + + if H0 is not None: + state.update_params({"H0": H0}) + else: + H0 = state.cosmo.H0 + + mean_dm_cosmic = get_mean_DM(z, state) + + for k, F in enumerate(Fs): + + sigma = F / np.sqrt(z) + C0 = fC0(sigma) + pdelta = zdm.pcosmic.pcosmic(deltas, z, F, C0) + + # Perform a bootstrap CI for `niter_per_F` iterations + lower_cis_at_F = np.zeros(niter_per_F) + for i in range(niter_per_F): + sample = np.random.choice( + deltas, p=pdelta / np.sum(pdelta), size=ns_per_F, replace=True + ) + lower_cis_at_F[i] = lower_ci(sample) * mean_dm_cosmic + + # Get the mean lower 95 pct CI from the bootstrap + lower_cis[k] = np.mean(lower_cis_at_F) + + return {"F": Fs, "lower.ci": lower_cis, "H0": np.ones(n) * H0} + + +def lowerCI_H0( + H0s, + F=None, + z=0.5, + deltas=np.linspace(0.01, 5, 200), + niter_per_H0=1000, + ns_per_H0=1000, +): + + z = np.array(z).reshape(1) + + n = len(H0s) + + lower_cis = np.zeros(n) + + state = State() + + if F is not None: + state.update_params({"F": F}) + else: + F = state.IGM.F + + sigma = F / np.sqrt(z) + C0 = fC0(sigma) + pdelta = zdm.pcosmic.pcosmic(deltas, z, F, C0) + + for k, H0 in enumerate(H0s): + + state.update_params({"H0": H0}) + + mean_dm_cosmic = get_mean_DM(z, state) + + # Perform a bootstrap CI for `niter_per_F` iterations + lower_cis_at_H0 = np.zeros(niter_per_H0) + for i in range(niter_per_H0): + + sample = np.random.choice( + deltas, p=pdelta / np.sum(pdelta), size=ns_per_H0, replace=True + ) + + lower_cis_at_H0[i] = lower_ci(sample) * mean_dm_cosmic + + # Get the mean lower 95 pct CI from the bootstrap + lower_cis[k] = np.mean(lower_cis_at_H0) + + return {"H0": H0s, "lower.ci": lower_cis, "F": np.ones(n) * F} + + +def make_plots_F( + Fs, + H0=None, + z=0.5, + deltas=np.linspace(0.01, 5, 200), + niter_per_F=1000, + ns_per_F=1000, + outfile="F_plot.png", +): + + df = lowerCI_F( + Fs, + H0=H0, + z=0.5, + deltas=np.linspace(0.01, 5, 200), + niter_per_F=1000, + ns_per_F=1000, + ) + + H0 = df["H0"][0] + + fig, ax = plt.subplots(dpi=200) + ax.scatter(df["F"], df["lower.ci"]) + ax.set_title(f"H0 = {H0}, z = {z}") + ax.set_xlabel(f"$F$") + ax.set_ylabel(f"$p(\Delta)$") + plt.savefig(outfile) + + +def make_plots_H0( + H0s, + F=None, + z=0.5, + deltas=np.linspace(0.01, 5, 200), + niter_per_H0=1000, + ns_per_H0=1000, + outfile="H0_plot.png", +): + + df = lowerCI_H0( + H0s, + F=F, + z=0.5, + deltas=np.linspace(0.01, 5, 200), + niter_per_H0=1000, + ns_per_H0=1000, + ) + + F = df["F"][0] + + fig, ax = plt.subplots(dpi=200) + ax.scatter(df["H0"], df["lower.ci"]) + ax.set_title(f"F = {F}, z = {z}") + ax.set_xlabel(f"$H_0$") + ax.set_ylabel(f"$p(\Delta)$") + plt.savefig(outfile) + + +# make_plots_F(np.linspace(0.1, 1, 20), z=0.5, outfile="F_plot_z_0.5.png") +# make_plots_H0(np.linspace(50, 80, 20), z=0.5, outfile="H0_plot_z_0.5.png") + +# make_plots_F(np.linspace(0.1, 1, 20), z=0.25, outfile="F_plot_z_0.25.png") +# make_plots_H0(np.linspace(50, 80, 20), z=0.25, outfile="H0_plot_z_0.25.png") + +# make_plots_F(np.linspace(0.1, 1, 20), z=0.1, outfile="F_plot_z_0.1.png") +# make_plots_H0(np.linspace(50, 80, 20), z=0.1, outfile="H0_plot_z_0.1.png") + +# make_plots_F(np.linspace(0.1, 1, 20), z=1.5, outfile="F_plot_z_1.5.png") +# make_plots_H0(np.linspace(50, 80, 20), z=1.5, outfile="H0_plot_z_1.5.png") + +make_plots_F(np.linspace(0.1, 1, 20), H0=55, z=0.25, outfile="F_plot_z_0.25_alt.png") +make_plots_H0(np.linspace(50, 80, 20), F=0.8, z=0.25, outfile="H0_plot_z_0.25_alt.png") From 93f44e98cd983ef7eff5f203789aa85c26ca1c01 Mon Sep 17 00:00:00 2001 From: Jay-MBP Date: Tue, 2 Aug 2022 21:45:42 -0700 Subject: [PATCH 040/104] fix label in lower_CI.py --- papers/F/Analysis/CRACO/Contour/lower_CI.py | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/papers/F/Analysis/CRACO/Contour/lower_CI.py b/papers/F/Analysis/CRACO/Contour/lower_CI.py index 3bcb86e2..18906dfb 100644 --- a/papers/F/Analysis/CRACO/Contour/lower_CI.py +++ b/papers/F/Analysis/CRACO/Contour/lower_CI.py @@ -135,7 +135,7 @@ def make_plots_F( ax.scatter(df["F"], df["lower.ci"]) ax.set_title(f"H0 = {H0}, z = {z}") ax.set_xlabel(f"$F$") - ax.set_ylabel(f"$p(\Delta)$") + ax.set_ylabel(f"Lower CI of $p(\Delta)$") plt.savefig(outfile) @@ -164,21 +164,21 @@ def make_plots_H0( ax.scatter(df["H0"], df["lower.ci"]) ax.set_title(f"F = {F}, z = {z}") ax.set_xlabel(f"$H_0$") - ax.set_ylabel(f"$p(\Delta)$") + ax.set_ylabel(f"Lower CI of $p(\Delta)$") plt.savefig(outfile) -# make_plots_F(np.linspace(0.1, 1, 20), z=0.5, outfile="F_plot_z_0.5.png") -# make_plots_H0(np.linspace(50, 80, 20), z=0.5, outfile="H0_plot_z_0.5.png") +make_plots_F(np.linspace(0.1, 1, 20), z=0.5, outfile="F_plot_z_0.5.png") +make_plots_H0(np.linspace(50, 80, 20), z=0.5, outfile="H0_plot_z_0.5.png") -# make_plots_F(np.linspace(0.1, 1, 20), z=0.25, outfile="F_plot_z_0.25.png") -# make_plots_H0(np.linspace(50, 80, 20), z=0.25, outfile="H0_plot_z_0.25.png") +make_plots_F(np.linspace(0.1, 1, 20), z=0.25, outfile="F_plot_z_0.25.png") +make_plots_H0(np.linspace(50, 80, 20), z=0.25, outfile="H0_plot_z_0.25.png") -# make_plots_F(np.linspace(0.1, 1, 20), z=0.1, outfile="F_plot_z_0.1.png") -# make_plots_H0(np.linspace(50, 80, 20), z=0.1, outfile="H0_plot_z_0.1.png") +make_plots_F(np.linspace(0.1, 1, 20), z=0.1, outfile="F_plot_z_0.1.png") +make_plots_H0(np.linspace(50, 80, 20), z=0.1, outfile="H0_plot_z_0.1.png") -# make_plots_F(np.linspace(0.1, 1, 20), z=1.5, outfile="F_plot_z_1.5.png") -# make_plots_H0(np.linspace(50, 80, 20), z=1.5, outfile="H0_plot_z_1.5.png") +make_plots_F(np.linspace(0.1, 1, 20), z=1.5, outfile="F_plot_z_1.5.png") +make_plots_H0(np.linspace(50, 80, 20), z=1.5, outfile="H0_plot_z_1.5.png") make_plots_F(np.linspace(0.1, 1, 20), H0=55, z=0.25, outfile="F_plot_z_0.25_alt.png") make_plots_H0(np.linspace(50, 80, 20), F=0.8, z=0.25, outfile="H0_plot_z_0.25_alt.png") From d44264f7c97f372e28a990992e57ba6a353454d5 Mon Sep 17 00:00:00 2001 From: Jay-MBP Date: Wed, 3 Aug 2022 22:49:12 -0700 Subject: [PATCH 041/104] create lower CI grid function --- papers/F/Analysis/CRACO/Contour/lower_CI.py | 77 ++++++++++++++++++--- 1 file changed, 67 insertions(+), 10 deletions(-) diff --git a/papers/F/Analysis/CRACO/Contour/lower_CI.py b/papers/F/Analysis/CRACO/Contour/lower_CI.py index 18906dfb..70c78246 100644 --- a/papers/F/Analysis/CRACO/Contour/lower_CI.py +++ b/papers/F/Analysis/CRACO/Contour/lower_CI.py @@ -168,17 +168,74 @@ def make_plots_H0( plt.savefig(outfile) -make_plots_F(np.linspace(0.1, 1, 20), z=0.5, outfile="F_plot_z_0.5.png") -make_plots_H0(np.linspace(50, 80, 20), z=0.5, outfile="H0_plot_z_0.5.png") +def lower_CI_grid( + H0s, + Fs, + deltas=np.linspace(0.01, 5, 200), + z=0.5, + niter_per_param=1000, + ns_per_param=1000, + make_plot=False, +): + + state = State() + + lower_cis = np.zeros((len(H0s), len(Fs))) + + for i, H0 in enumerate(H0s): + for j, F in enumerate(Fs): + state.update_params({"H0": H0, "F": F}) + + z = np.array(z).reshape(1) + mean_dm_cosmic = get_mean_DM(z, state) + + sigma = F / np.sqrt(z) + C0 = fC0(sigma) + pdelta = zdm.pcosmic.pcosmic(deltas, z, F, C0) + + lower_cis_at_pt = np.zeros(niter_per_param) + + for u in range(niter_per_param): + sample = np.random.choice( + deltas, p=pdelta / np.sum(pdelta), size=ns_per_param, replace=True + ) + + lower_cis_at_pt[u] = lower_ci(sample) * mean_dm_cosmic + + lower_cis[i, j] = np.mean(lower_cis_at_pt) + + if make_plot: + outfile = f"lower_CI_grid_z_{z}.png" + fig, ax = plt.subplots(dpi=200) + + x, y = np.meshgrid(H0s, Fs) + + c = ax.pcolormesh(x, y, lower_cis.T, cmap="jet") + plt.colorbar(c) + + ax.set_title(f"z = {z}") + + plt.savefig(outfile, bbox_inches="tight") + + return lower_cis + + +# make_plots_F(np.linspace(0.1, 1, 20), z=0.5, outfile="F_plot_z_0.5.png") +# make_plots_H0(np.linspace(50, 80, 20), z=0.5, outfile="H0_plot_z_0.5.png") + +# make_plots_F(np.linspace(0.1, 1, 20), z=0.25, outfile="F_plot_z_0.25.png") +# make_plots_H0(np.linspace(50, 80, 20), z=0.25, outfile="H0_plot_z_0.25.png") + +# make_plots_F(np.linspace(0.1, 1, 20), z=0.1, outfile="F_plot_z_0.1.png") +# make_plots_H0(np.linspace(50, 80, 20), z=0.1, outfile="H0_plot_z_0.1.png") -make_plots_F(np.linspace(0.1, 1, 20), z=0.25, outfile="F_plot_z_0.25.png") -make_plots_H0(np.linspace(50, 80, 20), z=0.25, outfile="H0_plot_z_0.25.png") +# make_plots_F(np.linspace(0.1, 1, 20), z=1.5, outfile="F_plot_z_1.5.png") +# make_plots_H0(np.linspace(50, 80, 20), z=1.5, outfile="H0_plot_z_1.5.png") -make_plots_F(np.linspace(0.1, 1, 20), z=0.1, outfile="F_plot_z_0.1.png") -make_plots_H0(np.linspace(50, 80, 20), z=0.1, outfile="H0_plot_z_0.1.png") +# make_plots_F(np.linspace(0.1, 1, 20), H0=55, z=0.25, outfile="F_plot_z_0.25_alt.png") +# make_plots_H0(np.linspace(50, 80, 20), F=0.8, z=0.25, outfile="H0_plot_z_0.25_alt.png") -make_plots_F(np.linspace(0.1, 1, 20), z=1.5, outfile="F_plot_z_1.5.png") -make_plots_H0(np.linspace(50, 80, 20), z=1.5, outfile="H0_plot_z_1.5.png") +lower_CI_grid( + H0s=np.linspace(55, 80, num=20), Fs=np.linspace(0.1, 0.8, num=20), make_plot=True +) -make_plots_F(np.linspace(0.1, 1, 20), H0=55, z=0.25, outfile="F_plot_z_0.25_alt.png") -make_plots_H0(np.linspace(50, 80, 20), F=0.8, z=0.25, outfile="H0_plot_z_0.25_alt.png") From eb2b29b236fbc942750d91465be5e5fd0f3c51cd Mon Sep 17 00:00:00 2001 From: Jay-MBP Date: Thu, 4 Aug 2022 21:47:45 -0700 Subject: [PATCH 042/104] formatting changes --- .../F/Analysis/CRACO/py/slurp_craco_cubes.py | 2 +- papers/F/Figures/py/figs_zdm_F_I.py | 111 +++++++++++------- 2 files changed, 71 insertions(+), 42 deletions(-) diff --git a/papers/F/Analysis/CRACO/py/slurp_craco_cubes.py b/papers/F/Analysis/CRACO/py/slurp_craco_cubes.py index b13a1b10..22bb32ef 100644 --- a/papers/F/Analysis/CRACO/py/slurp_craco_cubes.py +++ b/papers/F/Analysis/CRACO/py/slurp_craco_cubes.py @@ -19,7 +19,7 @@ def main(pargs): elif pargs.run == "F": # Emax input_file = "Cubes/craco_H0_F_cube.json" - prefix = "Cloud/OutputH0F/craco_H0_F" + prefix = "Cloud/Output/craco_H0_F" nsurveys = 1 # Run it diff --git a/papers/F/Figures/py/figs_zdm_F_I.py b/papers/F/Figures/py/figs_zdm_F_I.py index d9b95a43..13b516e8 100644 --- a/papers/F/Figures/py/figs_zdm_F_I.py +++ b/papers/F/Figures/py/figs_zdm_F_I.py @@ -24,6 +24,7 @@ def fig_craco_varyF_zDM( other_param="Emax", Aconts=[0.05], fuss_with_ticks: bool = False, + suppress_DM_host=False, ): """_summary_ @@ -81,8 +82,9 @@ def fig_craco_varyF_zDM( vparams["F"] = F # Sets the log-normal distribution for DM_host to ~0. - vparams["lmean"] = 1e-3 - vparams["lsigma"] = 0.1 + if suppress_DM_host: + vparams["lmean"] = 1e-3 + vparams["lsigma"] = 0.1 if other_param == "Emax": vparams["lEmax"] = fiducial_Emax + scl @@ -364,7 +366,7 @@ def fig_craco_fiducial_F( vparams = {"H0": fiducial_H0, "F": F} if H0 is not None: - vparams['H0'] = H0 + vparams["H0"] = H0 if suppress_DM_host: # Sets the log-normal distribution for DM_host to ~0. @@ -502,38 +504,67 @@ def fig_craco_fiducial_F( ### tests -# fig_craco_varyF_zDM("contours_varyF_H0.pdf", other_param="H0") - -# fig_craco_fiducial_F("fig_craco_fiducial_F_0.32.png", show_Macquart=True, F=0.32, suppress_DM_host=True) -# fig_craco_fiducial_F("fig_craco_fiducial_F_0.01.png", show_Macquart=True, F=0.01, suppress_DM_host=True) -# fig_craco_fiducial_F("fig_craco_fiducial_F_0.9.png", show_Macquart=True, F=0.9, suppress_DM_host=True) - -#fig_craco_fiducial_F("fig_craco_fiducial_dmhost_F_0.32.png", show_Macquart=False, F=0.32, suppress_DM_host=False) -#fig_craco_fiducial_F("fig_craco_fiducial_dmhost_F_0.82_H0_55.png", show_Macquart=False, F=0.82, H0=55., suppress_DM_host=False) -# fig_craco_fiducial_F("fig_craco_fiducial_dmhost_F_0.01.png", show_Macquart=True, F=0.01, suppress_DM_host=False) -# fig_craco_fiducial_F("fig_craco_fiducial_dmhost_F_0.9.png", show_Macquart=True, F=0.9, suppress_DM_host=False) - -# fig_craco_varyF_zDM("contours_varyF_lmean.pdf", other_param="lmean") - -# fig_varyF( -# "deg_basic.png", -# other_param="lmean", -# F_values=[0.01, 0.9], -# other_values=[None, None], -# lcolors=["r", "b"], -# lstyles=["-", "-"], -# DMmax=1800, -# ) - -# fig_varyF( -# "deg_other.png", -# other_param="lmean", -# F_values=[None, None], -# other_values=[2.5, 1.5], -# lcolors=["#e07a5f", "#81b29a"], -# lstyles=["-", "-"], -# DMmax=1800, -# ) +fig_craco_varyF_zDM("contours_varyF_H0.pdf", other_param="H0") +fig_craco_varyF_zDM( + "contours_varyF_H0_dmhost_suppressed.pdf", other_param="H0", suppress_DM_host=True +) + +fig_craco_fiducial_F( + "fig_craco_F_0.32_dmhost_suppressed.png", + show_Macquart=True, + F=0.32, + suppress_DM_host=True, +) +fig_craco_fiducial_F( + "fig_craco_F_0.01_dmhost_suppressed.png", + show_Macquart=True, + F=0.01, + suppress_DM_host=True, +) +fig_craco_fiducial_F( + "fig_craco_F_0.9_dmhost_suppressed.png", + show_Macquart=True, + F=0.9, + suppress_DM_host=True, +) + +fig_craco_fiducial_F( + "fig_craco_F_0.32.png", show_Macquart=False, F=0.32, suppress_DM_host=False +) + +fig_craco_fiducial_F( + "fig_craco_F_0.82_H0_55.png", + show_Macquart=False, + F=0.82, + H0=55.0, + suppress_DM_host=False, +) +fig_craco_fiducial_F( + "fig_craco_F_0.01.png", show_Macquart=True, F=0.01, suppress_DM_host=False +) +fig_craco_fiducial_F( + "fig_craco_F_0.9.png", show_Macquart=True, F=0.9, suppress_DM_host=False +) + +fig_varyF( + "fig_lmean_degeneracy_varyF.png", + other_param="lmean", + F_values=[0.01, 0.9], + other_values=[None, None], + lcolors=["r", "b"], + lstyles=["-", "-"], + DMmax=1800, +) + +fig_varyF( + "fig_lmean_degeneracy_varylm.png", + other_param="lmean", + F_values=[None, None], + other_values=[2.5, 1.5], + lcolors=["#e07a5f", "#81b29a"], + lstyles=["-", "-"], + DMmax=1800, +) # fig_varyF( # "test.png", @@ -545,12 +576,10 @@ def fig_craco_fiducial_F( # DMmax=1800, # ) -#fig_craco_varyF_zDM("strawberry.png", other_param="lmean") - # Fussing on the square -#fig_craco_fiducial_F("fig_craco_fiducial_dmhost_F_0.32.png", show_Macquart=False, F=0.32, suppress_DM_host=False) -#fig_craco_fiducial_F("fig_craco_fiducial_dmhost_F_0.82_H0_55.png", show_Macquart=False, F=0.82, H0=55., suppress_DM_host=False) +# fig_craco_fiducial_F("fig_craco_fiducial_dmhost_F_0.32.png", show_Macquart=False, F=0.32, suppress_DM_host=False) +# fig_craco_fiducial_F("fig_craco_fiducial_dmhost_F_0.82_H0_55.png", show_Macquart=False, F=0.82, H0=55., suppress_DM_host=False) # iFRB = 0 -#fig_craco_fiducial_F("fig_craco_fiducial_dmhost_F_0.99_H0_55_i0.png", show_Macquart=False, F=0.99, H0=55., suppress_DM_host=False, iFRB=0) -fig_craco_fiducial_F("fig_craco_fiducial_dmhost_F_0.32_i0.png", show_Macquart=False, F=0.32, suppress_DM_host=False, iFRB=0) \ No newline at end of file +# fig_craco_fiducial_F("fig_craco_fiducial_dmhost_F_0.99_H0_55_i0.png", show_Macquart=False, F=0.99, H0=55., suppress_DM_host=False, iFRB=0) +# fig_craco_fiducial_F("fig_craco_fiducial_dmhost_F_0.32_i0.png", show_Macquart=False, F=0.32, suppress_DM_host=False, iFRB=0) From 39973b813dd0e08b87a7797d53e90a3a1edefec1 Mon Sep 17 00:00:00 2001 From: Jay-MBP Date: Thu, 4 Aug 2022 21:47:48 -0700 Subject: [PATCH 043/104] formatting --- .../CRACO/Cloud/deprecated/OutputH0F.tar.gz | Bin 0 -> 213618 bytes papers/F/Analysis/CRACO/py/cube_test.ipynb | 54 ++++++++++++++---- 2 files changed, 44 insertions(+), 10 deletions(-) create mode 100644 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z;a7mJU3A0;uG`RX3SlNw0Fseyz<0*auMK{Rrys$@<6%cyRut$K1GFVVTWaYmLUgua z0=AKnv6^ayC+Z{^fGIcZLv+HPR!Raof2d>|AiyXM#C@TB@%k1m+)UYBY|Yu_rKG|X zs4(PbJDMLHD$@4%qG-K1l8{w8l0OyzqBXdrukci>NKt%*AU(e^I|}W0BNY@^TED(M z#ER5x3+iKiXiOsYRa Upa1^+KmX@{0Vn=NLI5xf08ol5%K!iX literal 0 HcmV?d00001 diff --git a/papers/F/Analysis/CRACO/py/cube_test.ipynb b/papers/F/Analysis/CRACO/py/cube_test.ipynb index 76acc3db..77d2c81f 100644 --- a/papers/F/Analysis/CRACO/py/cube_test.ipynb +++ b/papers/F/Analysis/CRACO/py/cube_test.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -12,25 +12,25 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ - "cube = np.load(\"../Cubes/craco_mini_cube.npz\")" + "cube = np.load(\"../Cubes/craco_H0_F_cube.npz\")" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(10, 25, 3, 5, 20, 5, 5, 15)" + "(50, 50)" ] }, - "execution_count": 5, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -42,7 +42,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -52,7 +52,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -62,11 +62,45 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ - "ll[np.isnan(ll)]=-1e99" + "ll[np.isnan(ll)]=-1e99\n", + "ll -= ll.max()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "\n", + "im=ax.imshow(ll.T,cmap='jet',origin='lower', \n", + " interpolation='None', extent=[F.min()-dF/2, F.max()+dF/2, 55.-dH/2, 80+dH/2], aspect='auto', vmin=-4., vmax=0.)\n", + "# Color bar\n", + "cbar=plt.colorbar(im,fraction=0.046, shrink=1.2,aspect=15,pad=0.05)\n", + "cbar.set_label(r'$\\Delta$ Log10 Likelihood')\n", + "\n", + "ax.set_xlabel('F')\n", + "ax.set_ylabel('H0 (km/s/Mpc)')\n", + "plt.savefig('fig_H0_vs_F.png', dpi=200)\n", + "plt.show()\n" ] }, { From 230e608aa8e75ade8c0508804a8699b3fc876efb Mon Sep 17 00:00:00 2001 From: Jay-MBP Date: Fri, 5 Aug 2022 10:44:10 -0700 Subject: [PATCH 044/104] 1D pdelta plots --- papers/F/Analysis/CRACO/Contour/lower_CI.py | 1 - papers/F/Analysis/CRACO/Contour/pdelta.py | 55 +++++++++++++++++++++ 2 files changed, 55 insertions(+), 1 deletion(-) create mode 100644 papers/F/Analysis/CRACO/Contour/pdelta.py diff --git a/papers/F/Analysis/CRACO/Contour/lower_CI.py b/papers/F/Analysis/CRACO/Contour/lower_CI.py index 70c78246..57ce1dcd 100644 --- a/papers/F/Analysis/CRACO/Contour/lower_CI.py +++ b/papers/F/Analysis/CRACO/Contour/lower_CI.py @@ -1,4 +1,3 @@ -from webbrowser import get import numpy as np import zdm import matplotlib.pyplot as plt diff --git a/papers/F/Analysis/CRACO/Contour/pdelta.py b/papers/F/Analysis/CRACO/Contour/pdelta.py new file mode 100644 index 00000000..a57d8701 --- /dev/null +++ b/papers/F/Analysis/CRACO/Contour/pdelta.py @@ -0,0 +1,55 @@ +import numpy as np +import zdm +import matplotlib.pyplot as plt +from frb.dm import cosmic +from zdm.pcosmic import pcosmic, get_mean_DM +from zdm.parameters import State +import scipy.stats + +fC0 = cosmic.grab_C0_spline() + + +def makePDeltaPlot_F(deltas, F, z, outfile=None): + sigma = F / np.sqrt(z) + C0 = fC0(sigma) + pdelta = zdm.pcosmic.pcosmic(deltas, z, F, C0) + fig, ax = plt.subplots(dpi=200) + ax.plot(deltas, pdelta, c="k") + if outfile is None: + outfile = f"pdelta_{F}_{z}.png" + ax.set_xlabel(r"$\Delta$") + ax.set_ylabel(r"$p(\Delta)$") + ax.set_title(f"F={F}, z={z}") + plt.savefig(outfile) + + +def test(deltas, Fs, z, colors, outfile=None): + + fig, ax = plt.subplots(figsize=(5, 4), dpi=200) + + for i, F in enumerate(Fs): + sigma = F / np.sqrt(z) + C0 = fC0(sigma) + pdelta = zdm.pcosmic.pcosmic(deltas, z, F, C0) + ax.plot(deltas, pdelta, c=colors[i], label=f"F = {F}") + + if outfile is None: + outfile = f"pdelta_test.png" + ax.set_xlabel(r"$\Delta$") + ax.set_ylabel(r"$p(\Delta)$") + ax.set_title(f"z={z}") + ax.legend() + plt.savefig(outfile, bbox_inches="tight") + + +# makePDeltaPlot_F(np.linspace(0.01, 2.5, 100), 0.32, 1) +# makePDeltaPlot_F(np.linspace(0.01, 2.5, 100), 0.01, 1) +# makePDeltaPlot_F(np.linspace(0.01, 2.5, 100), 1, 1) + +test( + np.linspace(0.01, 2.5, 300), + [0.01, 0.32, 0.5, 0.8], + z=0.3, + colors=["r", "orange", "y", "g", "b"], +) + From a8ce8599b33ea972c3c66ad47b01d0661a25d0cd Mon Sep 17 00:00:00 2001 From: Jay-MBP Date: Sun, 4 Sep 2022 20:19:38 -0400 Subject: [PATCH 045/104] add lmean/sigma cube --- .../F/Analysis/CRACO/Cloud/run_craco_lm_F.py | 131 +++++++++ papers/F/Analysis/CRACO/Contour/lower_CI.py | 28 +- .../Analysis/CRACO/Cubes/craco_lm_F_cube.json | 38 +++ .../CRACO/Cubes/craco_lm_F_state.json | 57 ++++ .../F/Analysis/CRACO/Fussing_on_F_lmean.ipynb | 269 ++++++++++++++++++ .../F/Analysis/CRACO/py/craco_qck_explore.py | 3 + .../F/Analysis/CRACO/py/slurp_craco_cubes.py | 14 +- 7 files changed, 534 insertions(+), 6 deletions(-) create mode 100644 papers/F/Analysis/CRACO/Cloud/run_craco_lm_F.py create mode 100644 papers/F/Analysis/CRACO/Cubes/craco_lm_F_cube.json create mode 100644 papers/F/Analysis/CRACO/Cubes/craco_lm_F_state.json create mode 100644 papers/F/Analysis/CRACO/Fussing_on_F_lmean.ipynb diff --git a/papers/F/Analysis/CRACO/Cloud/run_craco_lm_F.py b/papers/F/Analysis/CRACO/Cloud/run_craco_lm_F.py new file mode 100644 index 00000000..96690202 --- /dev/null +++ b/papers/F/Analysis/CRACO/Cloud/run_craco_lm_F.py @@ -0,0 +1,131 @@ +""" Run a Nautilus test """ + +# It should be possible to remove all the matplotlib calls from this +# but in the current implementation it is not removed. +import argparse +import numpy as np +import os, sys +from pkg_resources import resource_filename + +from concurrent.futures import ProcessPoolExecutor +import subprocess + +from zdm import iteration as it +from zdm import io + +from IPython import embed + + +def main( + pargs, + pfile: str, + oproot: str, + NFRB: int = None, + iFRB: int = 0, + outdir: str = "Output", +): + + # Generate the folder? + if not os.path.isdir(outdir): + os.mkdir(outdir) + + ############## Load up ############## + input_dict = io.process_jfile(pfile) + + # Deconstruct the input_dict + state_dict, cube_dict, vparam_dict = it.parse_input_dict(input_dict) + + npoints = np.array([item["n"] for key, item in vparam_dict.items()]) + ntotal = int(np.prod(np.abs(npoints))) + + # Total number of CPUs to be running on this Cube + total_ncpu = pargs.ncpu if pargs.total_ncpu is None else pargs.total_ncpu + batch = 1 if pargs.batch is None else pargs.batch + + nper_cpu = ntotal // total_ncpu + if int(ntotal / total_ncpu) != nper_cpu: + raise IOError(f"Ncpu={total_ncpu} must divide evenly into ntotal={ntotal}") + + survey_file = os.path.join( + resource_filename("zdm", "craco"), "MC_F", "Surveys", "F_0.32_survey" + ) + commands = [] + for kk in range(pargs.ncpu): + line = [] + # Which CPU is running out of the total? + iCPU = (batch - 1) * pargs.ncpu + kk + outfile = os.path.join(outdir, oproot.replace(".csv", f"{iCPU+1}.csv")) + # Command + line = [ + "zdm_build_cube", + "-n", + f"{iCPU+1}", + "-m", + f"{nper_cpu}", + "-o", + f"{outfile}", + "-s", + f"{survey_file}", + "--clobber", + "-p", + f"{pfile}", + ] + # NFRB? + if NFRB is not None: + line += [f"--NFRB", f"{NFRB}"] + # iFRB? + if iFRB > 0: + line += [f"--iFRB", f"{iFRB}"] + # Finish + # line += ' & \n' + commands.append(line) + + # Launch em! + processes = [] + + for command in commands: + # Popen + print(f"Running this command: {' '.join(command)}") + pw = subprocess.Popen(command) + processes.append(pw) + + # Wait on em! + for pw in processes: + pw.wait() + + print("All done!") + + +def parse_option(): + # test for command-line arguments here + parser = argparse.ArgumentParser() + parser.add_argument( + "-n", + "--ncpu", + type=int, + required=True, + help="Number of CPUs to run on (might be split in batches)", + ) + parser.add_argument( + "-t", + "--total_ncpu", + type=int, + required=False, + help="Total number of CPUs to run on (might be split in batches)", + ) + parser.add_argument( + "-b", "--batch", type=int, default=1, required=False, help="Batch number" + ) + # parser.add_argument('--NFRB',type=int,required=False,help="Number of FRBs to analzye") + # parser.add_argument('--iFRB',type=int,default=0,help="Initial FRB to run from") + args = parser.parse_args() + + return args + + +if __name__ == "__main__": + # get the argument of training. + pfile = "../Cubes/craco_lm_F_cube.json" + oproot = "craco_lm_F.csv" + pargs = parse_option() + main(pargs, pfile, oproot, NFRB=100, iFRB=100) diff --git a/papers/F/Analysis/CRACO/Contour/lower_CI.py b/papers/F/Analysis/CRACO/Contour/lower_CI.py index 70c78246..d55b0e9b 100644 --- a/papers/F/Analysis/CRACO/Contour/lower_CI.py +++ b/papers/F/Analysis/CRACO/Contour/lower_CI.py @@ -205,15 +205,17 @@ def lower_CI_grid( lower_cis[i, j] = np.mean(lower_cis_at_pt) if make_plot: - outfile = f"lower_CI_grid_z_{z}.png" + outfile = f"lower_CI_grid_z_{z[0]}.png" fig, ax = plt.subplots(dpi=200) x, y = np.meshgrid(H0s, Fs) - c = ax.pcolormesh(x, y, lower_cis.T, cmap="jet") - plt.colorbar(c) + c = ax.pcolormesh(x, y, lower_cis.T, cmap="jet", shading="auto") + plt.colorbar(c, label="DM (Lower CI)") - ax.set_title(f"z = {z}") + ax.set_title(f"z = {z[0]}") + ax.set_xlabel(f"$H_0$") + ax.set_ylabel(f"$F$") plt.savefig(outfile, bbox_inches="tight") @@ -236,6 +238,22 @@ def lower_CI_grid( # make_plots_H0(np.linspace(50, 80, 20), F=0.8, z=0.25, outfile="H0_plot_z_0.25_alt.png") lower_CI_grid( - H0s=np.linspace(55, 80, num=20), Fs=np.linspace(0.1, 0.8, num=20), make_plot=True + H0s=np.linspace(55, 80, num=20), + Fs=np.linspace(0.01, 1, num=20), + z=0.5, + make_plot=True, +) + +lower_CI_grid( + H0s=np.linspace(55, 80, num=20), + Fs=np.linspace(0.01, 1, num=20), + z=0.15, + make_plot=True, ) +lower_CI_grid( + H0s=np.linspace(55, 80, num=20), + Fs=np.linspace(0.01, 1, num=20), + z=0.05, + make_plot=True, +) diff --git a/papers/F/Analysis/CRACO/Cubes/craco_lm_F_cube.json b/papers/F/Analysis/CRACO/Cubes/craco_lm_F_cube.json new file mode 100644 index 00000000..47d0574f --- /dev/null +++ b/papers/F/Analysis/CRACO/Cubes/craco_lm_F_cube.json @@ -0,0 +1,38 @@ +{ + "state": { + "energy": { + "luminosity_function": 2 + }, + "FRBdemo": { + "alpha_method": 1 + }, + "cosmo": { + "fix_Omega_b_h2": true + } + }, + "cube": { + "parameter_order": [ + "lC", + "F", + "lmean" + ] + }, + "F": { + "DC": "IGM", + "min": 0.01, + "max": 1.0, + "n": 50 + }, + "lmean": { + "DC": "host", + "min": 1.7, + "max": 2.5, + "n": 50 + }, + "lC": { + "DC": "FRBdemo", + "min": -0.911, + "max": -0.911, + "n": -1 + } +} \ No newline at end of file diff --git a/papers/F/Analysis/CRACO/Cubes/craco_lm_F_state.json b/papers/F/Analysis/CRACO/Cubes/craco_lm_F_state.json new file mode 100644 index 00000000..38ba399e --- /dev/null +++ b/papers/F/Analysis/CRACO/Cubes/craco_lm_F_state.json @@ -0,0 +1,57 @@ +{ + "FRBdemo": { + "alpha_method": 1, + "lC": 1, + "sfr_n": 0.73, + "source_evolution": 0 + }, + "IGM": { + "F": 0.32 + }, + "MW": { + "DMhalo": 50, + "ISM": 35.0 + }, + "analysis": { + "NewGrids": true, + "sprefix": "Std" + }, + "beam": { + "Bmethod": 2, + "Bthresh": 0 + }, + "cosmo": { + "H0": 67.66, + "Omega_b": 0.04897, + "Omega_b_h2": 0.0224178568132, + "Omega_k": 0.0, + "Omega_lambda": 0.6888463055445441, + "Omega_m": 0.30966, + "fix_Omega_b_h2": true + }, + "energy": { + "alpha": 0.65, + "gamma": -1.01, + "lEmax": 41.4, + "lEmin": 30, + "luminosity_function": 2 + }, + "host": { + "lmean": 2.18, + "lsigma": 0.48 + }, + "scat": { + "Sfnorm": 600, + "Sfpower": -4.0, + "Slogmean": 0.7, + "Slogsigma": 1.9 + }, + "width": { + "Wbins": 10, + "Wlogmean": 1.70267, + "Wlogsigma": 0.899148, + "Wmethod": 2, + "Wscale": 2, + "Wthresh": 0.5 + } +} \ No newline at end of file diff --git a/papers/F/Analysis/CRACO/Fussing_on_F_lmean.ipynb b/papers/F/Analysis/CRACO/Fussing_on_F_lmean.ipynb new file mode 100644 index 00000000..51bc6bb9 --- /dev/null +++ b/papers/F/Analysis/CRACO/Fussing_on_F_lmean.ipynb @@ -0,0 +1,269 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "8e9d01fb-000b-4558-80e8-27688eafa19e", + "metadata": {}, + "source": [ + "# Quick check" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "7a372b56-1bb5-40be-bdf8-4129f926399e", + "metadata": {}, + "outputs": [], + "source": [ + "# imports\n", + "import numpy as np\n", + "import pandas\n", + "\n", + "import seaborn as sns\n", + "\n", + "from matplotlib import pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "id": "8ef3b730-fea3-40aa-9b7d-34814e9c2024", + "metadata": {}, + "source": [ + "## Load" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "654b497a-e590-4762-a0d2-4ce1c84306dc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['ll',\n", + " 'lC',\n", + " 'params',\n", + " 'pzDM',\n", + " 'pDM',\n", + " 'pDMz',\n", + " 'pz',\n", + " 'F',\n", + " 'lmean',\n", + " 'lls0',\n", + " 'P_zDM0',\n", + " 'P_n0',\n", + " 'P_s0',\n", + " 'N0']" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cube = np.load('Cubes/craco_lm_F_cube.npz')\n", + "list(cube.keys())" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "5a1f38c3-2276-4db4-8b85-b562c218f260", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(50, 50)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "LL = cube['ll']\n", + "LL.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "ffb3969c-843c-4186-8e8b-71132b959441", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "-573.6371" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.nanmax(LL[:,0])" + ] + }, + { + "cell_type": "markdown", + "id": "77e3c617-d266-4a7f-940f-170549619732", + "metadata": {}, + "source": [ + "## Parse" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "b7267261-fb2f-4686-9521-559730c3b3ed", + "metadata": {}, + "outputs": [], + "source": [ + "F = cube['F']\n", + "lm = cube['lmean']\n", + "#\n", + "dF = F[1]-F[0]\n", + "dlm = lm[1] - lm[0]" + ] + }, + { + "cell_type": "markdown", + "id": "59934a22-f603-4c5a-b5b1-86b4cf7b0ac8", + "metadata": {}, + "source": [ + "## Plot" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "adf1fdda-aa1c-4ee2-850d-82f36e5835c3", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.clf()\n", + "ax = plt.gca()\n", + "ax.plot(LL[:,0])\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "9b85c517-7fcc-408c-bc68-8f85639b5201", + "metadata": {}, + "source": [ + "## Show it all" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "24b700b6-1433-4a73-bde8-b5f9f9b5fd9c", + "metadata": {}, + "outputs": [], + "source": [ + "nans = np.isnan(LL)\n", + "LL_clean = LL.copy()\n", + "LL_clean[nans] = -9e9\n", + "#\n", + "LL_clean -= LL_clean.max()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "493e0416-168e-438a-9d29-40b095dbccbf", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'lmean')" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.clf()\n", + "ax=plt.gca()\n", + "#\n", + "im = plt.imshow(LL_clean.T, origin='lower', vmin=-4., vmax=0., cmap='jet',\n", + " extent=[F.min()-dF/2, F.max()+dF/2, 1.7-dlm/2, 2.5+dlm/2], aspect='auto')\n", + "# Color bar\n", + "cbar=plt.colorbar(im,fraction=0.046, shrink=1.2,aspect=15,pad=0.05)\n", + "cbar.set_label(r'$\\Delta$ Log10 Likelihood')\n", + "#\n", + "ax.set_xlabel('F')\n", + "ax.set_ylabel('lmean')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1e57d2a5-f711-4e40-bcf0-4de0576ec60a", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.8.5 ('base')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + }, + "vscode": { + "interpreter": { + "hash": "9731a3b7ebb41545a410367c249afbc4842fd5740da82f1bd29e6ac1d7253e6e" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/papers/F/Analysis/CRACO/py/craco_qck_explore.py b/papers/F/Analysis/CRACO/py/craco_qck_explore.py index 509fdf64..1d08ac59 100644 --- a/papers/F/Analysis/CRACO/py/craco_qck_explore.py +++ b/papers/F/Analysis/CRACO/py/craco_qck_explore.py @@ -22,6 +22,9 @@ def main(pargs): elif pargs.run == "F": scube = "H0_F" outdir = "H0_F/" + elif pargs.run == "lmF": + scube = "lm_F" + outdir = "lm_F/" elif pargs.run == "full": scube = "full" outdir = "Full/" diff --git a/papers/F/Analysis/CRACO/py/slurp_craco_cubes.py b/papers/F/Analysis/CRACO/py/slurp_craco_cubes.py index b13a1b10..a16e6a12 100644 --- a/papers/F/Analysis/CRACO/py/slurp_craco_cubes.py +++ b/papers/F/Analysis/CRACO/py/slurp_craco_cubes.py @@ -26,6 +26,18 @@ def main(pargs): analyze_cube.slurp_cube( input_file, prefix, "Cubes/craco_H0_F_cube.npz", nsurveys ) + + elif pargs.run == "lmF": + # Emax + input_file = "Cubes/craco_lm_F_cube.json" + prefix = "Cloud/Output/craco_lm_F" + nsurveys = 1 + + # Run it + analyze_cube.slurp_cube( + input_file, prefix, "Cubes/craco_lm_F_cube.npz", nsurveys + ) + elif pargs.run == "mini": # Emax input_file = "Cubes/craco_mini_cube.json" @@ -128,4 +140,4 @@ def parse_option(): # python py/slurp_craco_cubes.py mini # python py/slurp_craco_cubes.py another_full -# python py/slurp_craco_cubes.py F \ No newline at end of file +# python py/slurp_craco_cubes.py F From 16f3ae6fdb97a1d8b3eb02bd31aa056b7d6fd6d9 Mon Sep 17 00:00:00 2001 From: Jay-MBP Date: Sun, 4 Sep 2022 20:28:58 -0400 Subject: [PATCH 046/104] marginalize H0 --- papers/F/Analysis/CRACO/marginalize.ipynb | 170 ++++++++++------------ 1 file changed, 77 insertions(+), 93 deletions(-) diff --git a/papers/F/Analysis/CRACO/marginalize.ipynb b/papers/F/Analysis/CRACO/marginalize.ipynb index f435112f..e6f81e94 100644 --- a/papers/F/Analysis/CRACO/marginalize.ipynb +++ b/papers/F/Analysis/CRACO/marginalize.ipynb @@ -16,8 +16,7 @@ "metadata": {}, "outputs": [], "source": [ - "cube_dir = \"../CRACO/Cubes/craco_mini_cube.npz\"\n", - "cube_data = np.load(cube_dir)" + "cube_dir = \"../CRACO/Cubes/craco_mini_cube.npz\"" ] }, { @@ -26,127 +25,112 @@ "metadata": {}, "outputs": [], "source": [ - "def get_param_values(data,params):\n", - " \"\"\"\n", - " Gets the unique values of the data from a cube output\n", - " Currently the parameter order is hard-coded\n", - " \n", - " \"\"\"\n", - " # gets unique values for each axis\n", - " param_vals=[]\n", - " #param_list=[data[\"lEmax\"],data[\"H0\"],data[\"alpha\"],data[\"gamma\"],data[\"sfr_n\"],data[\"lmean\"],data[\"lsigma\"]]\n", - " #for col in param_list:\n", - " for param in params:\n", - " col=data[param]\n", - " unique=np.unique(col)\n", - " param_vals.append(unique) \n", - " return param_vals" + "cube=np.load(cube_dir)\n", + "ivalues, posteriors, weighted_posteriors=ac.get_bayesian_data(cube['ll'])" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ll\n", + "lC\n", + "params\n", + "pzDM\n", + "pDM\n", + "pDMz\n", + "pz\n", + "lEmax\n", + "H0\n", + "alpha\n", + "gamma\n", + "sfr_n\n", + "lmean\n", + "lsigma\n", + "F\n", + "lls0\n", + "P_zDM0\n", + "P_n0\n", + "P_s0\n", + "N0\n" + ] + } + ], "source": [ - "ll = cube_data[\"ll\"]\n", - "params = cube_data[\"params\"]\n", - "param_vals = get_param_values(cube_data, params)" + "for key in cube.keys():\n", + " print(key)" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 16, "metadata": {}, - "outputs": [], - "source": [ - "uvals=[]\n", - "latexnames=[]\n", - "for ip,param in enumerate(cube_data[\"params\"]):\n", - " # switches for alpha\n", - " if param==\"alpha\":\n", - " uvals.append(cube_data[param]*-1.)\n", - " else:\n", - " uvals.append(cube_data[param])\n", - " if param==\"alpha\":\n", - " latexnames.append('$\\\\alpha$')\n", - " ialpha=ip\n", - " elif param==\"F\":\n", - " latexnames.append('$F$')\n", - " elif param==\"lEmax\":\n", - " latexnames.append('$\\\\log_{10} E_{\\\\rm max}$')\n", - " elif param==\"H0\":\n", - " latexnames.append('$H_0$')\n", - " elif param==\"gamma\":\n", - " latexnames.append('$\\\\gamma$')\n", - " elif param==\"sfr_n\":\n", - " latexnames.append('$n_{\\\\rm sfr}$')\n", - " elif param==\"lmean\":\n", - " latexnames.append('$\\\\mu_{\\\\rm host}$')\n", - " elif param==\"lsigma\":\n", - " latexnames.append('$\\\\sigma_{\\\\rm host}$')" - ] + "outputs": [ + { + "data": { + "text/plain": [ + "array([55. , 56.04166667, 57.08333333, 58.125 , 59.16666667,\n", + " 60.20833333, 61.25 , 62.29166667, 63.33333333, 64.375 ,\n", + " 65.41666667, 66.45833333, 67.5 , 68.54166667, 69.58333333,\n", + " 70.625 , 71.66666667, 72.70833333, 73.75 , 74.79166667,\n", + " 75.83333333, 76.875 , 77.91666667, 78.95833333, 80. ])" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ - "deprecated, uw_vectors, wvectors=ac.get_bayesian_data(cube_data[\"ll\"])" + "h0_idx = np.where(cube[\"params\"] == \"H0\")[0][0]" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ - "list2 = []\n", - "vals2 = []\n", - "\n", - "for i, vec in enumerate(wvectors):\n", - " n_idx = np.argmax(vec)\n", - " val = uvals[i][n_idx]\n", - "\n", - " if params[i] != \"F\":\n", - " list2.append(params[i])\n", - " vals2.append(val)\n", - " else:\n", - " iF = i\n" + "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 26, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "for i, p in enumerate(list2):\n", - " list3 = np.concatenate((list2[0:i], list2[i+1:]))\n", - " vals3 = np.concatenate((vals2[0:i], vals2[i+1:]))\n", - " arr = ac.get_slice_from_parameters(cube_data, list3, vals3)\n", - "\n", - " nans = np.isnan(arr)\n", - " arr[nans] = -9e9\n", - "\n", - " arr -= np.max(arr)\n", - " arr = 10**arr\n", - " arr /= np.sum(arr)\n", - "\n", - " if i < iF:\n", - " modi = i\n", - " else:\n", - " modi = i+1\n", - " arr = arr.swapaxes(0,1)\n", - "\n", - " savename = f\"2D Plots/{params[iF]}_{params[modi]}.pdf\"\n", - "\n", - " if params[modi] == \"alpha\":\n", - " arr = np.flip(arr, axis=0)\n", - " ac.make_2d_plot(arr, latexnames[modi], latexnames[iF], -param_vals[modi], param_vals[iF], savename=savename, norm=1)\n", - " else:\n", - " ac.make_2d_plot(arr, latexnames[modi], latexnames[iF], param_vals[modi], param_vals[iF], savename=savename, norm=1)" + "fig, ax = plt.subplots(dpi=200)\n", + "ax.plot(cube[\"H0\"],posteriors[h0_idx], c=\"k\")\n", + "ax.set_xlabel(\"$H_0$\")\n", + "ax.set_ylabel(\"$p(H_0)$\")\n", + "ax.set_ylim(0, 0.05)\n", + "plt.show()" ] }, { From 84ae53f30f6e0b76af1edcc56d5bc797632ed414 Mon Sep 17 00:00:00 2001 From: profxj Date: Sun, 18 Sep 2022 08:22:32 -0700 Subject: [PATCH 047/104] ready? --- papers/H0_I/Figures/py/figs_zdm_H0_I.py | 87 +++++++++++++++++++++++-- 1 file changed, 82 insertions(+), 5 deletions(-) diff --git a/papers/H0_I/Figures/py/figs_zdm_H0_I.py b/papers/H0_I/Figures/py/figs_zdm_H0_I.py index 3caf1a24..587b987b 100644 --- a/papers/H0_I/Figures/py/figs_zdm_H0_I.py +++ b/papers/H0_I/Figures/py/figs_zdm_H0_I.py @@ -3,6 +3,7 @@ from typing import IO import numpy as np from numpy.lib.function_base import percentile +from pkg_resources import resource_filename import scipy from scipy import stats @@ -534,6 +535,82 @@ def fig_fd_vs_z(outfile='fig_fd_vs_z.png'): plt.savefig(outfile, dpi=200) print(f"Wrote: {outfile}") + +def fig_best_fit_to_data(outfile='fig_best_fit_to_data.png'): + + # Initialise surveys and grids + + # The below is for private, unpublished FRBs. You will NOT see this in the repository! + names = ['CRAFT_ICS','CRAFT_ICS_892','CRAFT_ICS_1632'] + sdir= os.path.join(resource_filename('zdm', 'data'), 'Surveys') + + # if True, this generates a summed histogram of all the surveys, weighted by + # the observation time + sumit=True + + # approximate best-fit values from recent analysis + vparams = {} + vparams['H0'] = 73 + vparams['lEmax'] = 41.3 + vparams['gamma'] = -0.9 + vparams['alpha'] = 1 + vparams['sfr_n'] = 1.15 + vparams['lmean'] = 2.25 + vparams['lsigma'] = 0.55 + + zvals=[] + dmvals=[] + grids=[] + surveys=[] + nozlist=[] + for i,name in enumerate(names): + s,g = loading.survey_and_grid( + survey_name='private_'+name,NFRB=None,sdir=sdir) # should be equal to actual number of FRBs, but for this purpose it doesn't matter + grids.append(g) + surveys.append(s) + + # set up new parameters + g.update(vparams) + + # gets cumulative rate distribution + if i==0: + rtot = np.copy(g.rates)*s.TOBS + else: + rtot += g.rates*s.TOBS + + if name=='Arecibo': + # remove high DM vals from rates as per ALFA survey limit + delete=np.where(g.dmvals > 2038)[0] + g.rates[:,delete]=0. + + for iFRB in s.zlist: + zvals.append(s.Zs[iFRB]) + dmvals.append(s.DMEGs[iFRB]) + for dm in s.DMEGs[s.nozlist]: + nozlist.append(dm) + + ############# do 2D plots ########## + #misc_functions.plot_grid_2(g.rates,g.zvals,g.dmvals, + # name=opdir+name+'.pdf',norm=3,log=True,label='$\\log_{10} p({\\rm DM}_{\\rm EG},z)$ [a.u.]', + # project=False,FRBDM=s.DMEGs,FRBZ=s.frbs["Z"],Aconts=[0.01,0.1,0.5],zmax=1.5, + # DMmax=1500)#,DMlines=s.DMEGs[s.nozlist]) + + # does the final plot of all data + frbzvals=np.array(zvals) + frbdmvals=np.array(dmvals) + ############# do 2D plots ########## + misc_functions.plot_grid_2(g.rates,g.zvals,g.dmvals, + name=outfile, #opdir+'Fig5_combined_localised_FRBs.pdf', + norm=3,log=True, + label='$\\log_{10} p({\\rm DM}_{\\rm EG},z)$ [a.u.]', + project=False,FRBDM=frbdmvals,FRBZ=frbzvals,Aconts=[0.01,0.1,0.5], + zmax=1.5,DMmax=2000, + DMlines=None, + cmap='jet', data_clr='k') + + print(f"Wrote: {outfile}") + + #### ########################## ######################### def main(pargs): @@ -554,10 +631,8 @@ def main(pargs): # Vary H0, F - if pargs.figure == 'varyH0F': - fig_craco_varyH0_zDM(outfile='fig_craco_varyH0F.png', - other_param='F') - + if pargs.figure == "varyH0F": + fig_craco_varyH0_zDM(outfile="fig_craco_varyH0F.png", other_param="F") # H0 vs. Emax if pargs.figure == 'H0vsEmax': @@ -611,4 +686,6 @@ def parse_option(): # python py/figs_zdm_H0_I.py varyH0F # python py/figs_zdm_H0_I.py varyH0E_sz # python py/figs_zdm_H0_I.py varyH0E_sDM -# python py/figs_zdm_H0_I.py fd_vs_z \ No newline at end of file +# python py/figs_zdm_H0_I.py fd_vs_z + +# python py/figs_zdm_H0_I.py best_fit_to_data \ No newline at end of file From f00d3f44d915d94a73063a98658c962735d7e31d Mon Sep 17 00:00:00 2001 From: Clancy James Date: Wed, 21 Sep 2022 14:28:43 +0800 Subject: [PATCH 048/104] Added plotting file --- zdm/scripts/plot_limits_from_cube.py | 195 +++++++++++++++++++++++++++ 1 file changed, 195 insertions(+) create mode 100644 zdm/scripts/plot_limits_from_cube.py diff --git a/zdm/scripts/plot_limits_from_cube.py b/zdm/scripts/plot_limits_from_cube.py new file mode 100644 index 00000000..fa3580e8 --- /dev/null +++ b/zdm/scripts/plot_limits_from_cube.py @@ -0,0 +1,195 @@ +""" +This is a script to produce limit plots for a cube + +It produces three sets of plots: +- single parameter limits with a prior on H0 between Planck and SN1a values +- single parameter limits also showing results with priors on H0 equal to: + a) Planck + b) Reiss + c) No prior +- 2D correlation plots with no prior onH0 + +It also collects data to plot a result on H0 for best-fit values of all +other parameters, but currently does not produce that plot + +""" + +import numpy as np +import os +import zdm +from zdm import analyze_cube as ac + +from matplotlib import pyplot as plt + +def main(verbose=False): + + ######### sets the values of H0 for priors ##### + Planck_H0 = 67.4 + Planck_sigma = 0.5 + Reiss_H0 = 74.03 + Reiss_sigma = 1.42 + + ##### loads cube data ##### + cube='craco_mini_cube.npz' + data=np.load(cube) + if verbose: + for thing in data: + print(thing) + print(data["params"]) + + # gets values of cube parameters + #param_vals=get_param_values(data,verbose) + + # gets latex names + uvals,latexnames = get_names_values(data) + + ################ single plots, no priors ############ + deprecated,uw_vectors,wvectors=ac.get_bayesian_data(data["ll"]) + ac.do_single_plots(uvals,uw_vectors,None,data["params"],tag="",log=False,logspline=False,kind='linear',truth=None,dolevels=True,latexnames=latexnames) + + ########### H0 data for fixed values of other parameters ########### + # extracts best-fit values + list1=[] + vals1=[] + list2=[] + vals2=[] + vals3=[] + for i,vec in enumerate(uw_vectors): + n=np.argmax(vec) # selects the most likely value + val=uvals[i][n] + if data["params"][i] == "H0": + # enables us to select a slice corresponding to particular H0 values + list1.append(data["params"][i]) + vals1.append(Reiss_H0) + + vals3.append(Planck_H0) + + iH0=i # setting index for Hubble + else: + # enables us to select a slice correspondng to the best-fit values of all other params + # i.e. ignoring uncertainty in them + list2.append(data["params"][i]) + vals2.append(val) + + # gets the slice corresponding to specific values of H0 + Reiss_H0_selection=ac.get_slice_from_parameters(data,list1,vals1,verbose=True) + Planck_H0_selection=ac.get_slice_from_parameters(data,list1,vals3,verbose=True) + + # will have Bayesian limits on all parameters over everything but H0 + deprecated,ReissH0_vectors,deprecated=ac.get_bayesian_data(Reiss_H0_selection) + deprecated,PlanckH0_vectors,deprecated=ac.get_bayesian_data(Planck_H0_selection) + + # gets the slice corresponding to the best-fit values of all other parameters + # this is 1D, so is our limit on H0 keeping all others fixed + pH0_fixed=ac.get_slice_from_parameters(data,list2,vals2) + + ####### 1D plots for prior on H0 ######## + # generates plots for our standard prior on H0 only + # applies a prior on H0, which is flat between systematic differences, then falls off as a Gaussian either side + H0_dim=np.where(data["params"]=="H0")[0][0] + wlls = ac.apply_H0_prior(data["ll"],H0_dim,data["H0"],Planck_H0, + Planck_sigma, Reiss_H0, Reiss_sigma) + deprecated,wH0_vectors,wvectors=ac.get_bayesian_data(wlls) + ac.do_single_plots(uvals,wH0_vectors,None,data["params"],tag="wH0_",truth=None, + dolevels=True,latexnames=latexnames,logspline=False) + + + # now do this with others... + # builds others... + others=[] + for i,p in enumerate(data["params"]): + if i==iH0: + oset=None + others.append(oset) + else: + if i Date: Thu, 22 Sep 2022 16:53:14 -0400 Subject: [PATCH 049/104] implement logF --- .../CRACO/Cloud/run_craco_mini_logF.py | 130 ++++++ .../CRACO/Cubes/craco_mini_cube_logF.json | 80 ++++ papers/F/Figures/py/figs_zdm_F_I.py | 2 +- zdm/misc_functions.py | 7 +- zdm/parameters.py | 6 +- zdm/pcosmic.py | 408 ++++++++++-------- 6 files changed, 444 insertions(+), 189 deletions(-) create mode 100644 papers/F/Analysis/CRACO/Cloud/run_craco_mini_logF.py create mode 100644 papers/F/Analysis/CRACO/Cubes/craco_mini_cube_logF.json diff --git a/papers/F/Analysis/CRACO/Cloud/run_craco_mini_logF.py b/papers/F/Analysis/CRACO/Cloud/run_craco_mini_logF.py new file mode 100644 index 00000000..0322aa8d --- /dev/null +++ b/papers/F/Analysis/CRACO/Cloud/run_craco_mini_logF.py @@ -0,0 +1,130 @@ +""" Run a Nautilus test """ + +# It should be possible to remove all the matplotlib calls from this +# but in the current implementation it is not removed. +import argparse +import numpy as np +import os, sys +from pkg_resources import resource_filename + +from concurrent.futures import ProcessPoolExecutor +import subprocess + +from zdm import iteration as it +from zdm import io + +from IPython import embed + + +def main( + pargs, + pfile: str, + oproot: str, + NFRB: int = None, + iFRB: int = 0, + outdir: str = "Output", +): + + # Generate the folder? + if not os.path.isdir(outdir): + os.mkdir(outdir) + + ############## Load up ############## + input_dict = io.process_jfile(pfile) + + # Deconstruct the input_dict + state_dict, cube_dict, vparam_dict = it.parse_input_dict(input_dict) + + npoints = np.array([item["n"] for key, item in vparam_dict.items()]) + ntotal = int(np.prod(np.abs(npoints))) + + # Total number of CPUs to be running on this Cube + total_ncpu = pargs.ncpu if pargs.total_ncpu is None else pargs.total_ncpu + batch = 1 if pargs.batch is None else pargs.batch + + nper_cpu = ntotal // total_ncpu + if int(ntotal / total_ncpu) != nper_cpu: + raise IOError(f"Ncpu={total_ncpu} must divide evenly into ntotal={ntotal}") + + survey_file = os.path.join(resource_filename('zdm', 'craco'), + 'MC_F', 'Surveys', 'F_0.32_survey') + commands = [] + for kk in range(pargs.ncpu): + line = [] + # Which CPU is running out of the total? + iCPU = (batch - 1) * pargs.ncpu + kk + outfile = os.path.join(outdir, oproot.replace(".csv", f"{iCPU+1}.csv")) + # Command + line = [ + "zdm_build_cube", + "-n", + f"{iCPU+1}", + "-m", + f"{nper_cpu}", + "-o", + f"{outfile}", + "-s", + f"{survey_file}", + "--clobber", + "-p", + f"{pfile}", + ] + # NFRB? + if NFRB is not None: + line += [f"--NFRB", f"{NFRB}"] + # iFRB? + if iFRB > 0: + line += [f"--iFRB", f"{iFRB}"] + # Finish + # line += ' & \n' + commands.append(line) + + # Launch em! + processes = [] + + for command in commands: + # Popen + print(f"Running this command: {' '.join(command)}") + pw = subprocess.Popen(command) + processes.append(pw) + + # Wait on em! + for pw in processes: + pw.wait() + + print("All done!") + + +def parse_option(): + # test for command-line arguments here + parser = argparse.ArgumentParser() + parser.add_argument( + "-n", + "--ncpu", + type=int, + required=True, + help="Number of CPUs to run on (might be split in batches)", + ) + parser.add_argument( + "-t", + "--total_ncpu", + type=int, + required=False, + help="Total number of CPUs to run on (might be split in batches)", + ) + parser.add_argument( + "-b", "--batch", type=int, default=1, required=False, help="Batch number" + ) + # parser.add_argument('--NFRB',type=int,required=False,help="Number of FRBs to analzye") + # parser.add_argument('--iFRB',type=int,default=0,help="Initial FRB to run from") + args = parser.parse_args() + + return args + + +if __name__ == "__main__": + # get the argument of training. + pfile = "../Cubes/craco_mini_cube_logF.json" + oproot = "craco_mini.csv" + pargs = parse_option() + main(pargs, pfile, oproot, NFRB=100, iFRB=100) diff --git a/papers/F/Analysis/CRACO/Cubes/craco_mini_cube_logF.json b/papers/F/Analysis/CRACO/Cubes/craco_mini_cube_logF.json new file mode 100644 index 00000000..b03b639b --- /dev/null +++ b/papers/F/Analysis/CRACO/Cubes/craco_mini_cube_logF.json @@ -0,0 +1,80 @@ +{ + "state": { + "energy": { + "luminosity_function": 2 + }, + "FRBdemo": { + "alpha_method": 1 + }, + "cosmo": { + "fix_Omega_b_h2": true + } + }, + "cube": { + "parameter_order": [ + "lC", + "sfr_n", + "alpha", + "lEmax", + "lmean", + "lsigma", + "logF", + "gamma", + "H0" + ] + }, + "lEmax": { + "DC": "energy", + "min": 40.5, + "max": 42.5, + "n": 12 + }, + "H0": { + "DC": "cosmo", + "min": 55.0, + "max": 80.0, + "n": 25 + }, + "alpha": { + "DC": "energy", + "min": 0.2, + "max": 2.0, + "n": 3 + }, + "gamma": { + "DC": "energy", + "min": -0.5, + "max": -1.5, + "n": 8 + }, + "sfr_n": { + "DC": "FRBdemo", + "min": 0.0, + "max": 3.0, + "n": 20 + }, + "lmean": { + "DC": "host", + "min": 1.7, + "max": 2.5, + "n": 5 + }, + "lsigma": { + "DC": "host", + "min": 0.3, + "max": 0.7, + "n": 5 + }, + "logF": { + "DC": "IGM", + "min": -1.7, + "max": 0, + "n": 20 + }, + "lC": { + "DC": "FRBdemo", + "min": -0.911, + "max": -0.911, + "n": -1 + } +} \ No newline at end of file diff --git a/papers/F/Figures/py/figs_zdm_F_I.py b/papers/F/Figures/py/figs_zdm_F_I.py index 13b516e8..4c7a414a 100644 --- a/papers/F/Figures/py/figs_zdm_F_I.py +++ b/papers/F/Figures/py/figs_zdm_F_I.py @@ -205,7 +205,7 @@ def fig_varyF( survey, grid = analy_F_I.craco_mc_survey_grid() - fiducial_F = grid.state.IGM.F + fiducial_F = grid.state.IGM.logF fiducial_Emax = grid.state.energy.lEmax fiducial_H0 = grid.state.cosmo.H0 fiducial_lmean = grid.state.host.lmean diff --git a/zdm/misc_functions.py b/zdm/misc_functions.py index 66182f27..6ca3b46b 100644 --- a/zdm/misc_functions.py +++ b/zdm/misc_functions.py @@ -1771,15 +1771,16 @@ def get_zdm_grid(state:parameters.State, new=True, t0=time.process_time() # calculate constants for p_DM distribution if orig: - C0s=pcosmic.make_C0_grid(zvals,state.IGM.F) + C0s=pcosmic.make_C0_grid(zvals,state.IGM.logF) else: f_C0_3 = cosmic.grab_C0_spline() - sigma = state.IGM.F / np.sqrt(zvals) + actual_F = 10**(state.IGM.logF) + sigma = actual_F / np.sqrt(zvals) C0s = f_C0_3(sigma) # generate pDM grid using those COs zDMgrid=pcosmic.get_pDM_grid(state,dmvals,zvals,C0s,zlog=zlog) - metadata=np.array([nz,ndm,state.IGM.F]) + metadata=np.array([nz,ndm,state.IGM.logF]) if save: np.save(savefile,zDMgrid) np.save(datfile,metadata) diff --git a/zdm/parameters.py b/zdm/parameters.py index e9157a26..1d5bdb0c 100644 --- a/zdm/parameters.py +++ b/zdm/parameters.py @@ -130,11 +130,11 @@ class HostParams(data_class.myDataClass): # IGM parameters @dataclass class IGMParams(data_class.myDataClass): - F: float = field( + logF: float = field( default=0.32, - metadata={'help': 'F parameter in DM$_{\\rm cosmic}$ PDF for the Cosmic web', + metadata={'help': 'logF parameter in DM$_{\\rm cosmic}$ PDF for the Cosmic web', 'unit': '', - 'Notation': 'F', + 'Notation': 'logF', }) diff --git a/zdm/pcosmic.py b/zdm/pcosmic.py index b589ce45..9b50889e 100644 --- a/zdm/pcosmic.py +++ b/zdm/pcosmic.py @@ -7,37 +7,42 @@ ############################# +from fcntl import F_ADD_SEALS import sys -#sys.path.insert(1, '/Users/cjames/CRAFT/FRB_library/ne2001-master/src/ne2001') +# sys.path.insert(1, '/Users/cjames/CRAFT/FRB_library/ne2001-master/src/ne2001') import matplotlib.pyplot as plt import numpy as np from astropy.cosmology import FlatLambdaCDM -#from frb import dlas +# from frb import dlas from frb.dm import igm from zdm import cosmology as cos from zdm import parameters -#from zdm import c_code + +# from zdm import c_code import scipy as sp -#import astropy.units as u + +# import astropy.units as u from IPython import embed # these are fitted values, which are shown to best-match data -alpha=3 -beta=3 -#F=0.32 ##### feedback best fit, but FIT IT!!! ##### +alpha = 3 +beta = 3 +# F=0.32 ##### feedback best fit, but FIT IT!!! ##### + + +def p(DM, z, F): + mean = z * 1000 # NOT true, just testing! + delta = DM / mean + p = pcosmic(delta, z, F) -def p(DM,z,F): - mean=z*1000 #NOT true, just testing! - delta=DM/mean - p=pcosmic(delta,z,F) -def pcosmic(delta,z,F,C0): +def pcosmic(delta, z, logF, C0): """ Equation 4 page 33 delta: = DM_cosmic/ @@ -48,49 +53,56 @@ def pcosmic(delta,z,F,C0): A: arbitrary amplitude of relative probability, ignore... """ - global beta,alpha - sigma=F*z**-0.5 - return delta**-beta * np.exp(-(delta**-alpha - C0)**2./(2.*alpha**2*sigma**2)) + ### logF compensation + F = 10 ** (logF) + ### -def p_delta_DM(z,F,C0,deltas=None,dmin=1e-3,dmax=10,ndelta=10000): + global beta, alpha + sigma = F * z ** -0.5 + return delta ** -beta * np.exp( + -((delta ** -alpha - C0) ** 2.0) / (2.0 * alpha ** 2 * sigma ** 2) + ) + + +def p_delta_DM(z, F, C0, deltas=None, dmin=1e-3, dmax=10, ndelta=10000): """ Calculates probability distribution of delta DM as function of feedback redshift and the constant C0 """ if not deltas: - deltas=np.linspace(dmin,dmax,ndelta) - pdeltas=pcosmic(deltas,z,F,C0) - return deltas,pdeltas - + deltas = np.linspace(dmin, dmax, ndelta) + pdeltas = pcosmic(deltas, z, F, C0) + return deltas, pdeltas -def iterate_C0(z,F,C0=1,Niter=10): +def iterate_C0(z, F, C0=1, Niter=10): """ Iteratively solves for C_0 as a function of z and F """ - dmin=1e-3 - dmax=10 - ndelta=10000 - deltas=np.linspace(dmin,dmax,ndelta) + dmin = 1e-3 + dmax = 10 + ndelta = 10000 + deltas = np.linspace(dmin, dmax, ndelta) for i in np.arange(Niter): - pdeltas=pcosmic(deltas,z,F,C0) - bin_w=deltas[1]-deltas[0] - norm=bin_w*np.sum(pdeltas) - mean=bin_w*np.sum(pdeltas*deltas)/norm - C0 += (mean-1.) - + pdeltas = pcosmic(deltas, z, F, C0) + bin_w = deltas[1] - deltas[0] + norm = bin_w * np.sum(pdeltas) + mean = bin_w * np.sum(pdeltas * deltas) / norm + C0 += mean - 1.0 + return C0 -def make_C0_grid(zeds,F): +def make_C0_grid(zeds, F): """ Pre-generates normalisation constants for C0 Does this from a grid of z and a given F """ - C0s=np.zeros([zeds.size]) - for i,z in enumerate(zeds): - C0s[i]=iterate_C0(z,F) + C0s = np.zeros([zeds.size]) + for i, z in enumerate(zeds): + C0s[i] = iterate_C0(z, F) return C0s -def get_mean_DM(zeds:np.ndarray, state:parameters.State): + +def get_mean_DM(zeds: np.ndarray, state: parameters.State): """ Gets mean average z to which can be applied deltas Args: @@ -101,21 +113,21 @@ def get_mean_DM(zeds:np.ndarray, state:parameters.State): np.ndarray: DM_cosmic """ # Generate the cosmology - cosmo = FlatLambdaCDM(H0=state.cosmo.H0, - Ob0=state.cosmo.Omega_b, - Om0=state.cosmo.Omega_m) + cosmo = FlatLambdaCDM( + H0=state.cosmo.H0, Ob0=state.cosmo.Omega_b, Om0=state.cosmo.Omega_m + ) # - zmax=zeds[-1] - nz=zeds.size - DMbar, zeval = igm.average_DM( - zmax, cosmo=cosmo, cumul=True, neval=nz+1) - + zmax = zeds[-1] + nz = zeds.size + DMbar, zeval = igm.average_DM(zmax, cosmo=cosmo, cumul=True, neval=nz + 1) + # Check assert np.allclose(zeds, zeval[1:]) - #wrong dimension + # wrong dimension return DMbar[1:].value - -def get_log_mean_DM(zeds:np.ndarray, state:parameters.State): + + +def get_log_mean_DM(zeds: np.ndarray, state: parameters.State): """ Gets mean average z to which can be applied deltas Does NOT assume that the zeds are linearly spaced. @@ -127,62 +139,72 @@ def get_log_mean_DM(zeds:np.ndarray, state:parameters.State): np.ndarray: DM_cosmic """ # Generate the cosmology - cosmo = FlatLambdaCDM(H0=state.cosmo.H0, - Ob0=state.cosmo.Omega_b, - Om0=state.cosmo.Omega_m) + cosmo = FlatLambdaCDM( + H0=state.cosmo.H0, Ob0=state.cosmo.Omega_b, Om0=state.cosmo.Omega_m + ) # - nz=zeds.size - dms=np.zeros([nz]) - for i,z in enumerate(zeds): + nz = zeds.size + dms = np.zeros([nz]) + for i, z in enumerate(zeds): # neval probably should be a function of max z - DMbar, zeval = igm.average_DM( - z, cosmo=cosmo, cumul=True, neval=nz+1)# - dms[i]=DMbar[-1].value - #wrong dimension - return dms + DMbar, zeval = igm.average_DM(z, cosmo=cosmo, cumul=True, neval=nz + 1) # + dms[i] = DMbar[-1].value + # wrong dimension + return dms + -def get_C0(z,F,zgrid,Fgrid,C0grid): +def get_C0(z, F, zgrid, Fgrid, C0grid): """ Takes a pre-generated table of C0 values, and calculates the p(DM) distribution based on this """ - - if z < zgrid[0] or z>zgrid[-1]: + + F = 10 ** F + Fgrid = 10 ** Fgrid + + if z < zgrid[0] or z > zgrid[-1]: print("Z value out of range") exit() - if F < Fgrid[0] or F>Fgrid[-1]: + if F < Fgrid[0] or F > Fgrid[-1]: print("F value out of range") exit() - - iz2=np.where(zgrid>z)[0] # gets first element greater than - iz1=iz2-1 - kz1=(zgrid[iz2]-z)/(zgrid[iz2]-zgrid[iz1]) - kz2=1.-kz1 - - iF2=np.where(Fgrid>F)[0] # gets first element greater than - iF1=iF2-1 - kF1=(Fgrid[iF2]-F)/(Fgrid[iF2]-Fgrid[iF1]) - kF2=1.-kF1 - - C0=kz1*kF1*C0grid[iz1,iF1] + kz2*kF1*C0grid[iz2,iF1] + kz1*kF2*C0grid[iz1,iF2] + kz2*kF2*C0grid[iz2,iF2] + + iz2 = np.where(zgrid > z)[0] # gets first element greater than + iz1 = iz2 - 1 + kz1 = (zgrid[iz2] - z) / (zgrid[iz2] - zgrid[iz1]) + kz2 = 1.0 - kz1 + + iF2 = np.where(Fgrid > F)[0] # gets first element greater than + iF1 = iF2 - 1 + kF1 = (Fgrid[iF2] - F) / (Fgrid[iF2] - Fgrid[iF1]) + kF2 = 1.0 - kF1 + + C0 = ( + kz1 * kF1 * C0grid[iz1, iF1] + + kz2 * kF1 * C0grid[iz2, iF1] + + kz1 * kF2 * C0grid[iz1, iF2] + + kz2 * kF2 * C0grid[iz2, iF2] + ) return C0 - - -def get_pDM(z,F,DMgrid,zgrid,Fgrid,C0grid,zlog=False): + + +def get_pDM(z, F, DMgrid, zgrid, Fgrid, C0grid, zlog=False): """ Gets pDM for an arbitrary z value zlog (bool): True if zs are log-spaced False if linearly spaced """ - C0=get_C0(z,F,zgrid,Fgrid,C0grid) + C0 = get_C0(z, F, zgrid, Fgrid, C0grid) if zlog: - DMbar=get_mean_DM(z) + DMbar = get_mean_DM(z) else: - DMbar=get_log_mean_DM(z) - deltas=DMgrid/DMbar #in units of fractional DM - pDM=pcosmic(deltas,z,F,C0) + DMbar = get_log_mean_DM(z) + deltas = DMgrid / DMbar # in units of fractional DM + pDM = pcosmic(deltas, z, F, C0) return pDM -def get_pDM_grid(state:parameters.State, DMgrid,zgrid,C0s, verbose=False, zlog=False): +def get_pDM_grid( + state: parameters.State, DMgrid, zgrid, C0s, verbose=False, zlog=False +): """ Gets pDM when the zvals are the same as the zgrid state C0grid: C0 values obtained by convergence @@ -194,23 +216,22 @@ def get_pDM_grid(state:parameters.State, DMgrid,zgrid,C0s, verbose=False, zlog=F """ - #added H0 dependency + # added H0 dependency if zlog: - DMbars=get_log_mean_DM(zgrid, state) + DMbars = get_log_mean_DM(zgrid, state) else: - DMbars=get_mean_DM(zgrid, state) - - pDMgrid=np.zeros([zgrid.size,DMgrid.size]) + DMbars = get_mean_DM(zgrid, state) + + pDMgrid = np.zeros([zgrid.size, DMgrid.size]) if verbose: - print("shapes and sizes are ",C0s.size,pDMgrid.shape,DMbars.shape) + print("shapes and sizes are ", C0s.size, pDMgrid.shape, DMbars.shape) # iterates over zgrid to calculate p_delta_DM - for i,z in enumerate(zgrid): - deltas=DMgrid/DMbars[i] # since pDM is defined such that the mean is 1 - - pDMgrid[i,:]=pcosmic(deltas,z,state.IGM.F,C0s[i]) - pDMgrid[i,:] /= np.sum(pDMgrid[i,:]) #normalisation - return pDMgrid + for i, z in enumerate(zgrid): + deltas = DMgrid / DMbars[i] # since pDM is defined such that the mean is 1 + pDMgrid[i, :] = pcosmic(deltas, z, state.IGM.logF, C0s[i]) + pDMgrid[i, :] /= np.sum(pDMgrid[i, :]) # normalisation + return pDMgrid #### defines DMx (excess contribution) #### @@ -218,40 +239,43 @@ def get_pDM_grid(state:parameters.State, DMgrid,zgrid,C0s, verbose=False, zlog=F # and either do or do not include the 1/x fatcor when converting log to dlin. # the DM part is because integral p(logDM)dlogDM = 1 # DM is normal, logmean and logsigma are natural logs of these parameters -def linlognormal_dlin(DM,*args): - ''' x values are in linear space, +def linlognormal_dlin(DM, *args): + """ x values are in linear space, args in logspace, - returns p dx ''' - logmean=args[0] - logsigma=args[1] - logDM=np.log(DM) - norm=(2.*np.pi)**-0.5/DM/logsigma - return norm*np.exp(-0.5*((logDM-logmean)/logsigma)**2) - -def loglognormal_dlog(logDM,*args): - '''x values, mean and sigma are already in logspace + returns p dx """ + logmean = args[0] + logsigma = args[1] + logDM = np.log(DM) + norm = (2.0 * np.pi) ** -0.5 / DM / logsigma + return norm * np.exp(-0.5 * ((logDM - logmean) / logsigma) ** 2) + + +def loglognormal_dlog(logDM, *args): + """x values, mean and sigma are already in logspace returns p dlogx That is, this function is simply a Gaussian, and the arguments happen to be in log space. - ''' - logmean=args[0] - logsigma=args[1] - norm=args[2] - return norm*np.exp(-0.5*((logDM-logmean)/logsigma)**2) + """ + logmean = args[0] + logsigma = args[1] + norm = args[2] + return norm * np.exp(-0.5 * ((logDM - logmean) / logsigma) ** 2) -def plot_mean(zvals,saveas, title="Mean DM"): - - mean=get_mean_DM(zvals) + +def plot_mean(zvals, saveas, title="Mean DM"): + + mean = get_mean_DM(zvals) plt.figure() - plt.xlabel('z') - plt.ylabel('$\\overline{\\rm DM}$') - plt.plot(zvals,mean,linewidth=2) + plt.xlabel("z") + plt.ylabel("$\\overline{\\rm DM}$") + plt.plot(zvals, mean, linewidth=2) plt.tight_layout() plt.title(title) plt.savefig(saveas) plt.show() plt.close() + def get_dm_mask(dmvals, params, zvals=None, plot=False): """ Generates a mask over which to integrate the lognormal Apply this mask as DM[i] = DM[set[i]]*mask[i] @@ -266,43 +290,47 @@ def get_dm_mask(dmvals, params, zvals=None, plot=False): params [vector, 2]: mean and sigma of the lognormal (log10) distribution """ - + if len(params) != 2: - raise ValueError("Incorrect number of DM parameters!",params," (expected log10mean, log10sigma)") + raise ValueError( + "Incorrect number of DM parameters!", + params, + " (expected log10mean, log10sigma)", + ) exit() - #expect the params to be log10 of actual values for simplicity + # expect the params to be log10 of actual values for simplicity # this converts to natural log - logmean=params[0]/0.4342944619 - logsigma=params[1]/0.4342944619 - - ddm=dmvals[1]-dmvals[0] - + logmean = params[0] / 0.4342944619 + logsigma = params[1] / 0.4342944619 + + ddm = dmvals[1] - dmvals[0] + ##### first generates a mask from the lognormal distribution ##### # in theory allows a mask up to length of the DN values, but will # get truncated # the first value has half weight (0 to 0.5) # the rest have width of 1 - mask=np.zeros([dmvals.size]) + mask = np.zeros([dmvals.size]) if zvals is not None: - ndm=dmvals.size - nz=zvals.size - mask=np.zeros([nz,ndm]) - for j,z in enumerate(zvals): + ndm = dmvals.size + nz = zvals.size + mask = np.zeros([nz, ndm]) + for j, z in enumerate(zvals): # with each redshift, we reduce the effects of a 'host' contribution by (1+z) # this means that we divide the value of logmean by 1/(1+z) # or equivalently, we multiply the ddm by this factor # here we choose the latter, but it is the same - mask[j,:]=integrate_pdm(ddm*(1.+z),ndm,logmean,logsigma) - mask[j,:] /= np.sum(mask[j,:]) # the mask must integrate to unity + mask[j, :] = integrate_pdm(ddm * (1.0 + z), ndm, logmean, logsigma) + mask[j, :] /= np.sum(mask[j, :]) # the mask must integrate to unity else: # do this for the z=0 case - #dmmin=0 - #dmmax=ddm*0.5 - #pdm,err=sp.integrate.quad(lognormal,dmmin,dmmax,args=(logmean,logsigma)) - #mask[0]=pdm - #csum=pdm - #imax=dmvals.size - #for i in np.arange(1,dmvals.size): + # dmmin=0 + # dmmax=ddm*0.5 + # pdm,err=sp.integrate.quad(lognormal,dmmin,dmmax,args=(logmean,logsigma)) + # mask[0]=pdm + # csum=pdm + # imax=dmvals.size + # for i in np.arange(1,dmvals.size): # if csum > CSUMCUT: # imax=i # break @@ -311,23 +339,30 @@ def get_dm_mask(dmvals, params, zvals=None, plot=False): # pdm,err=sp.integrate.quad(lognormal,dmmin,dmmax,args=(logmean,logsigma)) # csum += pdm # mask[i]=pdm - mask=integrate_pdm(ddm,dmvals.size,logmean,logsigma) - mask /= np.sum(mask) #ensures correct normalisation - #mask=mask[0:imax] - + mask = integrate_pdm(ddm, dmvals.size, logmean, logsigma) + mask /= np.sum(mask) # ensures correct normalisation + # mask=mask[0:imax] + if plot and (not (zvals is not None)): plt.figure() - plt.xlabel('${\\rm DM}_{\\rm X}$') - plt.ylabel('$p({\\rm DM}_{\\rm X})$') - label='$e^\\sigma='+str(np.exp(logsigma))[0:4]+'$, $e^\\mu='+str(np.exp(logmean))[0:4]+'$' - plt.plot(dmvals[0:imax],mask,linewidth=2,label=label) + plt.xlabel("${\\rm DM}_{\\rm X}$") + plt.ylabel("$p({\\rm DM}_{\\rm X})$") + label = ( + "$e^\\sigma=" + + str(np.exp(logsigma))[0:4] + + "$, $e^\\mu=" + + str(np.exp(logmean))[0:4] + + "$" + ) + plt.plot(dmvals[0:imax], mask, linewidth=2, label=label) plt.tight_layout() - plt.savefig('Plots/p_DM_X.pdf') + plt.savefig("Plots/p_DM_X.pdf") plt.close() return mask -def integrate_pdm(ddm,ndm,logmean,logsigma,quick=True,plot=False): - ''' + +def integrate_pdm(ddm, ndm, logmean, logsigma, quick=True, plot=False): + """ Assigns probabilities of DM smearing (e.g. due to the host galaxy contribution) to a histogram in dm space. @@ -350,50 +385,59 @@ def integrate_pdm(ddm,ndm,logmean,logsigma,quick=True,plot=False): Returns: mask (np.ndarray) - ''' + """ # do this for the z=0 case - - norm=(2.*np.pi)**-0.5/logsigma - - #csum=pdm - #imax=ndm - #if quick: + + norm = (2.0 * np.pi) ** -0.5 / logsigma + + # csum=pdm + # imax=ndm + # if quick: if plot or quick: # does not integrate, takes central values, here in linear space- tiny bias - dmmeans=np.linspace(ddm/2.,ndm*ddm-ddm/2.,ndm) - logdmmeans=np.log(dmmeans) - dlogs=ddm/dmmeans - m1=loglognormal_dlog(logdmmeans,logmean,logsigma,norm)*dlogs #worst errors in lowest bins - #else: + dmmeans = np.linspace(ddm / 2.0, ndm * ddm - ddm / 2.0, ndm) + logdmmeans = np.log(dmmeans) + dlogs = ddm / dmmeans + m1 = ( + loglognormal_dlog(logdmmeans, logmean, logsigma, norm) * dlogs + ) # worst errors in lowest bins + # else: if plot or not quick: - m2=np.zeros([ndm]) - args=(logmean,logsigma,norm) - pdm,err=sp.integrate.quad(loglognormal_dlog,np.log(ddm*0.5)-logsigma*10,np.log(ddm*0.5),args=args) - m2[0]=pdm - - for i in np.arange(1,ndm): - #if csum > CSUMCUT: + m2 = np.zeros([ndm]) + args = (logmean, logsigma, norm) + pdm, err = sp.integrate.quad( + loglognormal_dlog, + np.log(ddm * 0.5) - logsigma * 10, + np.log(ddm * 0.5), + args=args, + ) + m2[0] = pdm + + for i in np.arange(1, ndm): + # if csum > CSUMCUT: # imax=i # break - dmmin=(i-0.5)*ddm - dmmax=dmmin+ddm - pdm,err=sp.integrate.quad(loglognormal_dlog,np.log(dmmin),np.log(dmmax),args=args) - m2[i]=pdm + dmmin = (i - 0.5) * ddm + dmmax = dmmin + ddm + pdm, err = sp.integrate.quad( + loglognormal_dlog, np.log(dmmin), np.log(dmmax), args=args + ) + m2[i] = pdm if quick: - mask=m1 + mask = m1 else: - mask=m2 + mask = m2 if plot: plt.figure() - plt.plot(dmmeans,m2,label='quick') - plt.plot(dmmeans,mask,label='slow') - plt.xlabel('DM') - plt.ylabel('p(DM)') + plt.plot(dmmeans, m2, label="quick") + plt.plot(dmmeans, mask, label="slow") + plt.xlabel("DM") + plt.ylabel("p(DM)") plt.legend() - plt.xlim(0,1000) + plt.xlim(0, 1000) plt.tight_layout() - plt.savefig('dm_mask_comparison_plot.pdf') + plt.savefig("dm_mask_comparison_plot.pdf") plt.close() print("Generated plot of dm masks, exiting...") - exit() #quit to avoid infinite plots + exit() # quit to avoid infinite plots return mask From aefca4d936bb7e860223e532972482da9771cc1d Mon Sep 17 00:00:00 2001 From: Jay-MBP Date: Mon, 26 Sep 2022 22:06:10 -0400 Subject: [PATCH 050/104] adjust pDelta plots, survey file --- papers/F/Analysis/CRACO/Contour/pdelta.py | 32 +++- papers/F/Analysis/py/analy_F_I.py | 2 +- papers/F/Figures/py/figs_zdm_F_I.py | 208 ++++++++++++++-------- 3 files changed, 160 insertions(+), 82 deletions(-) diff --git a/papers/F/Analysis/CRACO/Contour/pdelta.py b/papers/F/Analysis/CRACO/Contour/pdelta.py index a57d8701..01c8abe5 100644 --- a/papers/F/Analysis/CRACO/Contour/pdelta.py +++ b/papers/F/Analysis/CRACO/Contour/pdelta.py @@ -42,14 +42,36 @@ def test(deltas, Fs, z, colors, outfile=None): plt.savefig(outfile, bbox_inches="tight") +def test2(deltas, Fs, z, colors, outfile=None): + + state = State() + + fig, ax = plt.subplots(figsize=(5, 4), dpi=200) + + for i, F in enumerate(Fs): + + state.update_params({"F": F}) + + sigma = F / np.sqrt(z) + C0 = fC0(sigma) + pdelta = zdm.pcosmic.pcosmic(deltas, z, F, C0) + mean_DM = get_mean_DM(np.array(z).reshape(1), state) + ax.plot(deltas * mean_DM, pdelta, c=colors[i], label=f"F = {F}") + + if outfile is None: + outfile = f"pdelta_test_2.png" + ax.set_xlabel(r"$\rm{DM_{EG}}$") + ax.set_ylabel(r"$p(\rm{DM_{EG}})$") + ax.set_title(f"z={z}") + ax.legend() + plt.savefig(outfile, bbox_inches="tight") + + # makePDeltaPlot_F(np.linspace(0.01, 2.5, 100), 0.32, 1) # makePDeltaPlot_F(np.linspace(0.01, 2.5, 100), 0.01, 1) # makePDeltaPlot_F(np.linspace(0.01, 2.5, 100), 1, 1) -test( - np.linspace(0.01, 2.5, 300), - [0.01, 0.32, 0.5, 0.8], - z=0.3, - colors=["r", "orange", "y", "g", "b"], +test2( + np.linspace(0.01, 2.5, 300), [0.01, 0.9], z=0.5, colors=["r", "orange"], ) diff --git a/papers/F/Analysis/py/analy_F_I.py b/papers/F/Analysis/py/analy_F_I.py index 12a8e289..361282d8 100644 --- a/papers/F/Analysis/py/analy_F_I.py +++ b/papers/F/Analysis/py/analy_F_I.py @@ -1,6 +1,6 @@ from zdm.craco import loading -fiducial_survey = "CRACO_std_May2022" +fiducial_survey = "CRACO_F_0.32_survey" def craco_mc_survey_grid(iFRB=100): diff --git a/papers/F/Figures/py/figs_zdm_F_I.py b/papers/F/Figures/py/figs_zdm_F_I.py index 13b516e8..e73b79a7 100644 --- a/papers/F/Figures/py/figs_zdm_F_I.py +++ b/papers/F/Figures/py/figs_zdm_F_I.py @@ -272,6 +272,27 @@ def fig_varyF( leg, _ = cs.legend_elements() legend_lines.append(leg[0]) + ### TEST + # Interpolators + f_DM = interp1d( + dmvals, np.arange(dmvals.size), fill_value="extrapolate", bounds_error=False + ) + f_z = interp1d( + zvals, np.arange(zvals.size), fill_value="extrapolate", bounds_error=False + ) + + cosmo = FlatLambdaCDM( + H0=grid.state.cosmo.H0, + Ob0=grid.state.cosmo.Omega_b, + Om0=grid.state.cosmo.Omega_m, + ) + + dms, zeval = figm.average_DM(2.0, cumul=True, cosmo=cosmo) + + l_mqr = ax.plot(f_z(zeval), f_DM(dms), ls="--", c=color, alpha=0.5) + + #### TEST END + if other_param == "Emax": labels.append( r"$F = $" + f"{F}, log " + r"$E_{\rm max}$" + f"= {vparams['lEmax']}" @@ -281,26 +302,26 @@ def fig_varyF( elif other_param == "lmean": labels.append(r"$F = $" + f"{F}, $\mu =$ {vparams['lmean']}") - # Interpolators - f_DM = interp1d( - dmvals, np.arange(dmvals.size), fill_value="extrapolate", bounds_error=False - ) - f_z = interp1d( - zvals, np.arange(zvals.size), fill_value="extrapolate", bounds_error=False - ) + # # Interpolators + # f_DM = interp1d( + # dmvals, np.arange(dmvals.size), fill_value="extrapolate", bounds_error=False + # ) + # f_z = interp1d( + # zvals, np.arange(zvals.size), fill_value="extrapolate", bounds_error=False + # ) - cosmo = FlatLambdaCDM( - H0=grid.state.cosmo.H0, - Ob0=grid.state.cosmo.Omega_b, - Om0=grid.state.cosmo.Omega_m, - ) + # cosmo = FlatLambdaCDM( + # H0=grid.state.cosmo.H0, + # Ob0=grid.state.cosmo.Omega_b, + # Om0=grid.state.cosmo.Omega_m, + # ) - dms, zeval = figm.average_DM(2.0, cumul=True, cosmo=cosmo) + # dms, zeval = figm.average_DM(2.0, cumul=True, cosmo=cosmo) - l_mqr = ax.plot(f_z(zeval), f_DM(dms), "k--") + # l_mqr = ax.plot(f_z(zeval), f_DM(dms), "k--") - legend_lines.append(l_mqr[0]) - labels.append("Macquart Relation") + # legend_lines.append(l_mqr[0]) + # labels.append("Macquart Relation") ax.legend(legend_lines, labels, loc="lower right") @@ -459,14 +480,14 @@ def fig_craco_fiducial_F( linewidth=2, label="Macquart relation (mean)", ) - plt.plot( - zeval, - DMEG_median, - color="gray", - linewidth=2, - ls="--", - label="Macquart relation (median)", - ) + # plt.plot( + # zeval, + # DMEG_median, + # color="gray", + # linewidth=2, + # ls="--", + # label="Macquart relation (median)", + # ) l = plt.legend(loc="lower right", fontsize=12) # l=plt.legend(bbox_to_anchor=(0.2, 0.8),fontsize=8) # for text in l.get_texts(): @@ -504,68 +525,103 @@ def fig_craco_fiducial_F( ### tests -fig_craco_varyF_zDM("contours_varyF_H0.pdf", other_param="H0") -fig_craco_varyF_zDM( - "contours_varyF_H0_dmhost_suppressed.pdf", other_param="H0", suppress_DM_host=True -) +# fig_craco_varyF_zDM("contours_varyF_H0.pdf", other_param="H0") +# fig_craco_varyF_zDM( +# "contours_varyF_H0_dmhost_suppressed.pdf", other_param="H0", suppress_DM_host=True +# ) -fig_craco_fiducial_F( - "fig_craco_F_0.32_dmhost_suppressed.png", - show_Macquart=True, - F=0.32, - suppress_DM_host=True, -) -fig_craco_fiducial_F( - "fig_craco_F_0.01_dmhost_suppressed.png", - show_Macquart=True, - F=0.01, - suppress_DM_host=True, -) -fig_craco_fiducial_F( - "fig_craco_F_0.9_dmhost_suppressed.png", - show_Macquart=True, - F=0.9, - suppress_DM_host=True, -) +# fig_craco_fiducial_F( +# "fig_craco_F_0.32_dmhost_suppressed.png", +# show_Macquart=True, +# F=0.32, +# suppress_DM_host=True, +# ) +# fig_craco_fiducial_F( +# "fig_craco_F_0.01_dmhost_suppressed.png", +# show_Macquart=True, +# F=0.01, +# suppress_DM_host=True, +# ) +# fig_craco_fiducial_F( +# "fig_craco_F_0.9_dmhost_suppressed.png", +# show_Macquart=True, +# F=0.9, +# suppress_DM_host=True, +# ) -fig_craco_fiducial_F( - "fig_craco_F_0.32.png", show_Macquart=False, F=0.32, suppress_DM_host=False -) +# fig_craco_fiducial_F( +# "fig_craco_F_0.32.png", show_Macquart=True, F=0.32, suppress_DM_host=False +# ) -fig_craco_fiducial_F( - "fig_craco_F_0.82_H0_55.png", - show_Macquart=False, - F=0.82, - H0=55.0, - suppress_DM_host=False, -) -fig_craco_fiducial_F( - "fig_craco_F_0.01.png", show_Macquart=True, F=0.01, suppress_DM_host=False -) -fig_craco_fiducial_F( - "fig_craco_F_0.9.png", show_Macquart=True, F=0.9, suppress_DM_host=False -) +# fig_craco_fiducial_F( +# "fig_craco_F_0.32_H0_55.png", +# show_Macquart=True, +# F=0.32, +# H0=55.0, +# suppress_DM_host=False, +# ) -fig_varyF( - "fig_lmean_degeneracy_varyF.png", - other_param="lmean", - F_values=[0.01, 0.9], - other_values=[None, None], - lcolors=["r", "b"], - lstyles=["-", "-"], - DMmax=1800, -) +# fig_craco_fiducial_F( +# "fig_craco_F_0.01.png", show_Macquart=True, F=0.01, suppress_DM_host=False +# ) +# fig_craco_fiducial_F( +# "fig_craco_F_0.9.png", show_Macquart=True, F=0.9, suppress_DM_host=False +# ) + +# fig_varyF( +# "fig_lmean_degeneracy_varyF.png", +# other_param="lmean", +# F_values=[0.01, 0.9], +# other_values=[None, None], +# lcolors=["r", "b"], +# lstyles=["-", "-"], +# DMmax=1800, +# ) + +# fig_varyF( +# "fig_lmean_degeneracy_varylm.png", +# other_param="lmean", +# F_values=[None, None], +# other_values=[2.5, 1.5], +# lcolors=["#e07a5f", "#81b29a"], +# lstyles=["-", "-"], +# DMmax=1800, +# ) + +### + +# fig_varyF( +# "fig_varyingF.png", +# other_param="H0", +# F_values=[0.01, 0.32, 0.5, 0.9], +# other_values=[None, None, None, None], +# lcolors=["#f72585", "#f8961e", "#3a0ca3", "#4895ef"], +# lstyles=["-", "-", "-", "-"], +# DMmax=1800, +# ) fig_varyF( - "fig_lmean_degeneracy_varylm.png", - other_param="lmean", - F_values=[None, None], - other_values=[2.5, 1.5], - lcolors=["#e07a5f", "#81b29a"], + "fig_H0_F_Degeneracy_.png", + other_param="H0", + F_values=[0.32, 0.82], + other_values=[None, 55], + lcolors=["#f72585", "#f8961e"], lstyles=["-", "-"], DMmax=1800, ) +# fig_varyF( +# "fig_varyingH0.png", +# other_param="H0", +# F_values=[0.32, 0.32, 0.32], +# other_values=[55, 67.4, 80], +# lcolors=["#f72585", "#f8961e", "#4895ef"], +# lstyles=["-", "-", "-"], +# DMmax=1800, +# ) + +### + # fig_varyF( # "test.png", # other_param="lmean", From b9905b251dca710724494e8dfa2c2bb8e79d3d63 Mon Sep 17 00:00:00 2001 From: Jay-MBP Date: Thu, 29 Sep 2022 15:05:18 -0400 Subject: [PATCH 051/104] Fixing logF in the cube json --- .../Analysis/CRACO/Cloud/run_craco_H0_logF.py | 131 + .../CRACO/Cubes/craco_H0_logF_cube.json | 38 + .../CRACO/Cubes/craco_H0_logF_state.json | 57 + .../F/Analysis/CRACO/py/craco_qck_explore.py | 3 + .../F/Analysis/CRACO/py/slurp_craco_cubes.py | 11 + zdm/grid.py | 94 +- zdm/misc_functions.py | 4132 ++++++++++------- zdm/pcosmic.py | 2 +- zdm/scripts/plot_limits_from_cube.py | 247 +- 9 files changed, 2790 insertions(+), 1925 deletions(-) create mode 100644 papers/F/Analysis/CRACO/Cloud/run_craco_H0_logF.py create mode 100644 papers/F/Analysis/CRACO/Cubes/craco_H0_logF_cube.json create mode 100644 papers/F/Analysis/CRACO/Cubes/craco_H0_logF_state.json diff --git a/papers/F/Analysis/CRACO/Cloud/run_craco_H0_logF.py b/papers/F/Analysis/CRACO/Cloud/run_craco_H0_logF.py new file mode 100644 index 00000000..e77b9c8b --- /dev/null +++ b/papers/F/Analysis/CRACO/Cloud/run_craco_H0_logF.py @@ -0,0 +1,131 @@ +""" Run a Nautilus test """ + +# It should be possible to remove all the matplotlib calls from this +# but in the current implementation it is not removed. +import argparse +import numpy as np +import os, sys +from pkg_resources import resource_filename + +from concurrent.futures import ProcessPoolExecutor +import subprocess + +from zdm import iteration as it +from zdm import io + +from IPython import embed + + +def main( + pargs, + pfile: str, + oproot: str, + NFRB: int = None, + iFRB: int = 0, + outdir: str = "Output", +): + + # Generate the folder? + if not os.path.isdir(outdir): + os.mkdir(outdir) + + ############## Load up ############## + input_dict = io.process_jfile(pfile) + + # Deconstruct the input_dict + state_dict, cube_dict, vparam_dict = it.parse_input_dict(input_dict) + + npoints = np.array([item["n"] for key, item in vparam_dict.items()]) + ntotal = int(np.prod(np.abs(npoints))) + + # Total number of CPUs to be running on this Cube + total_ncpu = pargs.ncpu if pargs.total_ncpu is None else pargs.total_ncpu + batch = 1 if pargs.batch is None else pargs.batch + + nper_cpu = ntotal // total_ncpu + if int(ntotal / total_ncpu) != nper_cpu: + raise IOError(f"Ncpu={total_ncpu} must divide evenly into ntotal={ntotal}") + + survey_file = os.path.join( + resource_filename("zdm", "craco"), "MC_F", "Surveys", "F_0.32_survey" + ) + commands = [] + for kk in range(pargs.ncpu): + line = [] + # Which CPU is running out of the total? + iCPU = (batch - 1) * pargs.ncpu + kk + outfile = os.path.join(outdir, oproot.replace(".csv", f"{iCPU+1}.csv")) + # Command + line = [ + "zdm_build_cube", + "-n", + f"{iCPU+1}", + "-m", + f"{nper_cpu}", + "-o", + f"{outfile}", + "-s", + f"{survey_file}", + "--clobber", + "-p", + f"{pfile}", + ] + # NFRB? + if NFRB is not None: + line += [f"--NFRB", f"{NFRB}"] + # iFRB? + if iFRB > 0: + line += [f"--iFRB", f"{iFRB}"] + # Finish + # line += ' & \n' + commands.append(line) + + # Launch em! + processes = [] + + for command in commands: + # Popen + print(f"Running this command: {' '.join(command)}") + pw = subprocess.Popen(command) + processes.append(pw) + + # Wait on em! + for pw in processes: + pw.wait() + + print("All done!") + + +def parse_option(): + # test for command-line arguments here + parser = argparse.ArgumentParser() + parser.add_argument( + "-n", + "--ncpu", + type=int, + required=True, + help="Number of CPUs to run on (might be split in batches)", + ) + parser.add_argument( + "-t", + "--total_ncpu", + type=int, + required=False, + help="Total number of CPUs to run on (might be split in batches)", + ) + parser.add_argument( + "-b", "--batch", type=int, default=1, required=False, help="Batch number" + ) + # parser.add_argument('--NFRB',type=int,required=False,help="Number of FRBs to analzye") + # parser.add_argument('--iFRB',type=int,default=0,help="Initial FRB to run from") + args = parser.parse_args() + + return args + + +if __name__ == "__main__": + # get the argument of training. + pfile = "../Cubes/craco_H0_logF_cube.json" + oproot = "craco_H0_logF.csv" + pargs = parse_option() + main(pargs, pfile, oproot, NFRB=100, iFRB=100) diff --git a/papers/F/Analysis/CRACO/Cubes/craco_H0_logF_cube.json b/papers/F/Analysis/CRACO/Cubes/craco_H0_logF_cube.json new file mode 100644 index 00000000..1c4da32b --- /dev/null +++ b/papers/F/Analysis/CRACO/Cubes/craco_H0_logF_cube.json @@ -0,0 +1,38 @@ +{ + "state": { + "energy": { + "luminosity_function": 2 + }, + "FRBdemo": { + "alpha_method": 1 + }, + "cosmo": { + "fix_Omega_b_h2": true + } + }, + "cube": { + "parameter_order": [ + "lC", + "logF", + "H0" + ] + }, + "logF": { + "DC": "IGM", + "min": -2, + "max": 0, + "n": 50 + }, + "H0": { + "DC": "cosmo", + "min": 55.0, + "max": 80.0, + "n": 50 + }, + "lC": { + "DC": "FRBdemo", + "min": -0.911, + "max": -0.911, + "n": -1 + } +} \ No newline at end of file diff --git a/papers/F/Analysis/CRACO/Cubes/craco_H0_logF_state.json b/papers/F/Analysis/CRACO/Cubes/craco_H0_logF_state.json new file mode 100644 index 00000000..bc5f08a8 --- /dev/null +++ b/papers/F/Analysis/CRACO/Cubes/craco_H0_logF_state.json @@ -0,0 +1,57 @@ +{ + "FRBdemo": { + "alpha_method": 1, + "lC": 1, + "sfr_n": 0.73, + "source_evolution": 0 + }, + "IGM": { + "logF": 0.32 + }, + "MW": { + "DMhalo": 50, + "ISM": 35.0 + }, + "analysis": { + "NewGrids": true, + "sprefix": "Std" + }, + "beam": { + "Bmethod": 2, + "Bthresh": 0 + }, + "cosmo": { + "H0": 67.66, + "Omega_b": 0.04897, + "Omega_b_h2": 0.0224178568132, + "Omega_k": 0.0, + "Omega_lambda": 0.6888463055445441, + "Omega_m": 0.30966, + "fix_Omega_b_h2": true + }, + "energy": { + "alpha": 0.65, + "gamma": -1.01, + "lEmax": 41.4, + "lEmin": 30, + "luminosity_function": 2 + }, + "host": { + "lmean": 2.18, + "lsigma": 0.48 + }, + "scat": { + "Sfnorm": 600, + "Sfpower": -4.0, + "Slogmean": 0.7, + "Slogsigma": 1.9 + }, + "width": { + "Wbins": 10, + "Wlogmean": 1.70267, + "Wlogsigma": 0.899148, + "Wmethod": 2, + "Wscale": 2, + "Wthresh": 0.5 + } +} \ No newline at end of file diff --git a/papers/F/Analysis/CRACO/py/craco_qck_explore.py b/papers/F/Analysis/CRACO/py/craco_qck_explore.py index 1d08ac59..bdf28c25 100644 --- a/papers/F/Analysis/CRACO/py/craco_qck_explore.py +++ b/papers/F/Analysis/CRACO/py/craco_qck_explore.py @@ -25,6 +25,9 @@ def main(pargs): elif pargs.run == "lmF": scube = "lm_F" outdir = "lm_F/" + elif pargs.run == "H0_logF": + scube = "H0_logF" + outdir = "H0_logF/" elif pargs.run == "full": scube = "full" outdir = "Full/" diff --git a/papers/F/Analysis/CRACO/py/slurp_craco_cubes.py b/papers/F/Analysis/CRACO/py/slurp_craco_cubes.py index 042a1f7c..72fc1d72 100644 --- a/papers/F/Analysis/CRACO/py/slurp_craco_cubes.py +++ b/papers/F/Analysis/CRACO/py/slurp_craco_cubes.py @@ -27,6 +27,17 @@ def main(pargs): input_file, prefix, "Cubes/craco_H0_F_cube.npz", nsurveys ) + elif pargs.run == "logF": + # Emax + input_file = "Cubes/craco_H0_logF_cube.json" + prefix = "Cloud/Output_logF_test/craco_H0_logF" + nsurveys = 1 + + # Run it + analyze_cube.slurp_cube( + input_file, prefix, "Cubes/craco_H0_logF_cube.npz", nsurveys + ) + elif pargs.run == "lmF": # Emax input_file = "Cubes/craco_lm_F_cube.json" diff --git a/zdm/grid.py b/zdm/grid.py index fec68da8..f1389a8d 100644 --- a/zdm/grid.py +++ b/zdm/grid.py @@ -8,6 +8,7 @@ from zdm import pcosmic from zdm import io + class Grid: """A class to hold a grid of z-dm plots @@ -16,10 +17,8 @@ class Grid: It also assumes a linear uniform grid. """ - - def __init__(self, survey, state, - zDMgrid, zvals, dmvals, smear_mask, - wdist): + + def __init__(self, survey, state, zDMgrid, zvals, dmvals, smear_mask, wdist): """ Class constructor. @@ -73,8 +72,6 @@ def __init__(self, survey, state, self.set_evolution() # sets star-formation rate scaling with z - here, no evoltion... self.calc_rates() # includes sfr smearing factors and pdv mult - - def init_luminosity_functions(self): """ Set the luminsoity function for FRB energetics """ if self.luminosity_function == 0: # Power-law @@ -93,18 +90,20 @@ def init_luminosity_functions(self): self.vector_cum_lf = energetics.vector_cum_gamma_spline self.array_diff_lf = energetics.array_diff_gamma self.vector_diff_lf = energetics.vector_diff_gamma - elif self.luminosity_function==3: # Linear + log10 - self.array_cum_lf=energetics.array_cum_gamma_linear - self.vector_cum_lf=energetics.vector_cum_gamma_linear - self.array_diff_lf=energetics.array_diff_gamma - self.vector_diff_lf=energetics.vector_diff_gamma + elif self.luminosity_function == 3: # Linear + log10 + self.array_cum_lf = energetics.array_cum_gamma_linear + self.vector_cum_lf = energetics.vector_cum_gamma_linear + self.array_diff_lf = energetics.array_diff_gamma + self.vector_diff_lf = energetics.vector_diff_gamma else: - raise ValueError("Luminosity function must be 0, not ",self.luminosity_function) - - def parse_grid(self,zDMgrid,zvals,dmvals): - self.grid=zDMgrid - self.zvals=zvals - self.dmvals=dmvals + raise ValueError( + "Luminosity function must be 0, not ", self.luminosity_function + ) + + def parse_grid(self, zDMgrid, zvals, dmvals): + self.grid = zDMgrid + self.zvals = zvals + self.dmvals = dmvals # self.check_grid() # self.calc_dV() @@ -268,29 +267,37 @@ def calc_pdv(self, beam_b=None, beam_o=None): # call log10 beam if self.use_log10: - new_thresh = np.log10(self.thresholds) # use when calling in log10 space conversion + new_thresh = np.log10( + self.thresholds + ) # use when calling in log10 space conversion main_beam_b = np.log10(main_beam_b) - for i,b in enumerate(main_beam_b): - for j,w in enumerate(self.eff_weights): - + for i, b in enumerate(main_beam_b): + for j, w in enumerate(self.eff_weights): # using log10 space conversion if self.use_log10: - thresh = new_thresh[j,:,:] - b - else: # original - thresh = self.thresholds[j,:,:]/b - - if j==0: - self.b_fractions[:,:,i] = self.beam_o[i]*w*self.array_cum_lf( - thresh,Emin,Emax, - self.state.energy.gamma, self.use_log10) + thresh = new_thresh[j, :, :] - b + else: # original + thresh = self.thresholds[j, :, :] / b + + if j == 0: + self.b_fractions[:, :, i] = ( + self.beam_o[i] + * w + * self.array_cum_lf( + thresh, Emin, Emax, self.state.energy.gamma, self.use_log10 + ) + ) else: - self.b_fractions[:,:,i] += self.beam_o[i]*w*self.array_cum_lf( - thresh,Emin,Emax, - self.state.energy.gamma, self.use_log10) - - + self.b_fractions[:, :, i] += ( + self.beam_o[i] + * w + * self.array_cum_lf( + thresh, Emin, Emax, self.state.energy.gamma, self.use_log10 + ) + ) + # here, b-fractions are unweighted according to the value of b. self.fractions = np.sum( self.b_fractions, axis=2 @@ -376,10 +383,9 @@ def calc_thresholds( # FRB width (nthresh) and DM. # We loop over nthesh and generate a NDM x Nz array for each for i in np.arange(self.nthresh): - self.thresholds[i,:,:]=np.outer(self.FtoE,Eff_thresh[i,:]) - - - def smear_dm(self,smear:np.ndarray):#,mean:float,sigma:float): + self.thresholds[i, :, :] = np.outer(self.FtoE, Eff_thresh[i, :]) + + def smear_dm(self, smear: np.ndarray): # ,mean:float,sigma:float): """ Smears DM using the supplied array. Example use: DMX contribution @@ -693,7 +699,7 @@ def update(self, vparams: dict, ALL=False, prev_grid=None): new_sfr_smear = True # IGM - if self.chk_upd_param("F", vparams, update=True): + if self.chk_upd_param("logF", vparams, update=True): get_zdm = True smear_dm = True # calc_thresh = False # JMB @@ -797,10 +803,12 @@ def update(self, vparams: dict, ALL=False, prev_grid=None): if calc_thresh or ALL: self.calc_thresholds( - self.F0,self.eff_table, bandwidth=self.bandwidth, - weights=self.eff_weights) - - + self.F0, + self.eff_table, + bandwidth=self.bandwidth, + weights=self.eff_weights, + ) + if calc_pdv or ALL: self.calc_pdv() diff --git a/zdm/misc_functions.py b/zdm/misc_functions.py index 6ca3b46b..97a922ca 100644 --- a/zdm/misc_functions.py +++ b/zdm/misc_functions.py @@ -26,386 +26,431 @@ from zdm import parameters -def marginalise(pset,grids,surveys,which,vals,disable=None,psnr=True,PenTypes=None,PenParams=None,Verbose=False,steps=None): +def marginalise( + pset, + grids, + surveys, + which, + vals, + disable=None, + psnr=True, + PenTypes=None, + PenParams=None, + Verbose=False, + steps=None, +): """ Calculates limits for a single variable """ - t0=time.process_time() - t1=t0 - lls=np.zeros([vals.size]) - psets=[] + t0 = time.process_time() + t1 = t0 + lls = np.zeros([vals.size]) + psets = [] if disable is not None: disable.append(which) else: - disable=[which] - steps=np.full([8],0.5) - for i,v in enumerate(vals): - pset[which]=v - print("Setting parameter ",which," = ",v) - - C_ll,C_p=it.my_minimise(pset,grids,surveys,disable=disable,psnr=psnr,PenTypes=PenTypes,PenParams=PenParams,Verbose=False,steps=steps) - steps=np.full([8],0.1) - print(i,v,C_ll,pset) - t1=time.process_time() + disable = [which] + steps = np.full([8], 0.5) + for i, v in enumerate(vals): + pset[which] = v + print("Setting parameter ", which, " = ", v) + + C_ll, C_p = it.my_minimise( + pset, + grids, + surveys, + disable=disable, + psnr=psnr, + PenTypes=PenTypes, + PenParams=PenParams, + Verbose=False, + steps=steps, + ) + steps = np.full([8], 0.1) + print(i, v, C_ll, pset) + t1 = time.process_time() psets.append(C_p) - lls[i]=C_ll - t2=time.process_time() - print("Iteration ",i," took ",t2-t1," seconds") - t1=t2 - print("Done - total time ",t1-t0," seconds") - psets=np.array(psets) - np.save('Marginalise1D/'+str(which)+'_lls.npy',lls) - np.save('Marginalise1D/'+str(which)+'_psets.npy',psets) + lls[i] = C_ll + t2 = time.process_time() + print("Iteration ", i, " took ", t2 - t1, " seconds") + t1 = t2 + print("Done - total time ", t1 - t0, " seconds") + psets = np.array(psets) + np.save("Marginalise1D/" + str(which) + "_lls.npy", lls) + np.save("Marginalise1D/" + str(which) + "_psets.npy", psets) -def get_source_counts(grid,plot=None,Slabel=None): +def get_source_counts(grid, plot=None, Slabel=None): """ Calculates the source-counts function for a given grid It does this in terms of p(SNR)dSNR """ # this is closely related to the likelihood for observing a given psnr! - + # calculate vector of grid thresholds - Emax=grid.Emax - Emin=grid.Emin - gamma=grid.gamma - - nsnr=71 - snrmin=0.001 - snrmax=1000. - ndm=grid.dmvals.size - snrs=np.logspace(0,2,nsnr) # histogram array of values for s=SNR/SNR_th - + Emax = grid.Emax + Emin = grid.Emin + gamma = grid.gamma + + nsnr = 71 + snrmin = 0.001 + snrmax = 1000.0 + ndm = grid.dmvals.size + snrs = np.logspace(0, 2, nsnr) # histogram array of values for s=SNR/SNR_th + # holds cumulative and differential source counts - cpsnrs=np.zeros([nsnr]) - psnrs=np.zeros([nsnr-1]) - + cpsnrs = np.zeros([nsnr]) + psnrs = np.zeros([nsnr - 1]) + # holds DM-dependent source counts - dmcpsnrs=np.zeros([nsnr,ndm]) - dmpsnrs=np.zeros([nsnr-1,ndm]) - - backup1=np.copy(grid.thresholds) - Emin=grid.Emin - Emax=grid.Emax - gamma=grid.gamma - + dmcpsnrs = np.zeros([nsnr, ndm]) + dmpsnrs = np.zeros([nsnr - 1, ndm]) + + backup1 = np.copy(grid.thresholds) + Emin = grid.Emin + Emax = grid.Emax + gamma = grid.gamma + # modifies grid to simplify beamshape - grid.beam_b=np.array([grid.beam_b[-1]]) - grid.beam_o=np.array([grid.beam_o[-1]]) - grid.b_fractions=None - - for i,s in enumerate(snrs): - - grid.thresholds=backup1*s - grid.calc_pdv(Emin,Emax,gamma) + grid.beam_b = np.array([grid.beam_b[-1]]) + grid.beam_o = np.array([grid.beam_o[-1]]) + grid.b_fractions = None + + for i, s in enumerate(snrs): + + grid.thresholds = backup1 * s + grid.calc_pdv(Emin, Emax, gamma) grid.calc_rates() - rates=grid.rates - dmcpsnrs[i,:]=np.sum(rates,axis=0) - cpsnrs[i]=np.sum(dmcpsnrs[i,:]) - + rates = grid.rates + dmcpsnrs[i, :] = np.sum(rates, axis=0) + cpsnrs[i] = np.sum(dmcpsnrs[i, :]) + # the last one contains cumulative values - for i,s in enumerate(snrs): - if i==0: + for i, s in enumerate(snrs): + if i == 0: continue - psnrs[i-1]=cpsnrs[i-1]-cpsnrs[i] - dmpsnrs[i-1,:]=dmcpsnrs[i-1,:]-dmcpsnrs[i,:] - - mod=1.5 - snrs=snrs[:-1] - imid=int((nsnr+1)/2) - xmid=snrs[imid] - ymid=psnrs[imid] - slopes=np.linspace(1.3,1.7,5) - ys=[] - for i,s in enumerate(slopes): - ys.append(ymid*xmid**s*snrs**-s) - + psnrs[i - 1] = cpsnrs[i - 1] - cpsnrs[i] + dmpsnrs[i - 1, :] = dmcpsnrs[i - 1, :] - dmcpsnrs[i, :] + + mod = 1.5 + snrs = snrs[:-1] + imid = int((nsnr + 1) / 2) + xmid = snrs[imid] + ymid = psnrs[imid] + slopes = np.linspace(1.3, 1.7, 5) + ys = [] + for i, s in enumerate(slopes): + ys.append(ymid * xmid ** s * snrs ** -s) + if plot is not None: - fixpoint=ys[0][0]*snrs[0]**mod + fixpoint = ys[0][0] * snrs[0] ** mod plt.figure() - plt.xscale('log') - plt.yscale('log') - plt.ylim(1,3) - plt.xlabel('$s=\\frac{\\rm SNR}{\\rm SNR_{\\rm th}}$') - plt.ylabel('$p(s) s^{1.5} d\\,\\log(s)$ [a.u.]') - plt.plot(snrs,psnrs*snrs**mod/fixpoint,label='Prediction ('+Slabel+')',color='black',linewidth=2) # this is in relative units - for i,s in enumerate(slopes): - plt.plot(snrs,ys[i]*snrs**mod/fixpoint,label='slope='+str(s)[0:3]) - ax=plt.gca() - #labels = [item.get_text() for item in ax.get_yticklabels()] - #print("Labels are ",labels) - #labels[0] = '1' - #labels[1] = '2' - #labels[2] = '3' - #ax.set_yticklabels(labels) - ax.set_yticks([1,2,3]) - ax.set_yticklabels(['1','2','3']) - plt.legend(fontsize=12)#,loc=[6,8]) + plt.xscale("log") + plt.yscale("log") + plt.ylim(1, 3) + plt.xlabel("$s=\\frac{\\rm SNR}{\\rm SNR_{\\rm th}}$") + plt.ylabel("$p(s) s^{1.5} d\\,\\log(s)$ [a.u.]") + plt.plot( + snrs, + psnrs * snrs ** mod / fixpoint, + label="Prediction (" + Slabel + ")", + color="black", + linewidth=2, + ) # this is in relative units + for i, s in enumerate(slopes): + plt.plot(snrs, ys[i] * snrs ** mod / fixpoint, label="slope=" + str(s)[0:3]) + ax = plt.gca() + # labels = [item.get_text() for item in ax.get_yticklabels()] + # print("Labels are ",labels) + # labels[0] = '1' + # labels[1] = '2' + # labels[2] = '3' + # ax.set_yticklabels(labels) + ax.set_yticks([1, 2, 3]) + ax.set_yticklabels(["1", "2", "3"]) + plt.legend(fontsize=12) # ,loc=[6,8]) plt.tight_layout() plt.savefig(plot) plt.close() - return snrs,psnrs,dmpsnrs - + return snrs, psnrs, dmpsnrs + + def get_test_pks_surveys(): - + # load Parkes data # generates a set of surveys with fake central beam histograms - pksa=survey.survey() - pksa.process_survey_file('Surveys/parkes_mb.dat') - pksa.meta["BEAM"]="a_b0" - pksa.init_beam(method=3,plot=True) # need more bins for Parkes! - - pkse=survey.survey() - pkse.process_survey_file('Surveys/parkes_mb.dat') - pkse.meta["BEAM"]="e_b0" - pkse.init_beam(method=3,plot=True) # need more bins for Parkes! - - pksk=survey.survey() - pksk.process_survey_file('Surveys/parkes_mb.dat') - pksk.meta["BEAM"]="k_b0" - pksk.init_beam(method=3,plot=True) # need more bins for Parkes! - - pksh=survey.survey() - pksh.process_survey_file('Surveys/parkes_mb.dat') - pksh.meta["BEAM"]="h_b0" - pksh.init_beam(method=3,plot=True) # need more bins for Parkes! - - pksl=survey.survey() - pksl.process_survey_file('Surveys/parkes_mb.dat') - pksl.meta["BEAM"]="l_b0" - pksl.init_beam(method=3,plot=True) # need more bins for Parkes! - - surveys=[pksa,pkse,pksk,pksh,pksl] + pksa = survey.survey() + pksa.process_survey_file("Surveys/parkes_mb.dat") + pksa.meta["BEAM"] = "a_b0" + pksa.init_beam(method=3, plot=True) # need more bins for Parkes! + + pkse = survey.survey() + pkse.process_survey_file("Surveys/parkes_mb.dat") + pkse.meta["BEAM"] = "e_b0" + pkse.init_beam(method=3, plot=True) # need more bins for Parkes! + + pksk = survey.survey() + pksk.process_survey_file("Surveys/parkes_mb.dat") + pksk.meta["BEAM"] = "k_b0" + pksk.init_beam(method=3, plot=True) # need more bins for Parkes! + + pksh = survey.survey() + pksh.process_survey_file("Surveys/parkes_mb.dat") + pksh.meta["BEAM"] = "h_b0" + pksh.init_beam(method=3, plot=True) # need more bins for Parkes! + + pksl = survey.survey() + pksl.process_survey_file("Surveys/parkes_mb.dat") + pksl.meta["BEAM"] = "l_b0" + pksl.init_beam(method=3, plot=True) # need more bins for Parkes! + + surveys = [pksa, pkse, pksk, pksh, pksl] return surveys - -def do_single_errors(grids,surveys,pset,outdir): + +def do_single_errors(grids, surveys, pset, outdir): """ iterates over sensible ranges of all single-parameter errors """ - + # for each parameter, investigate the best-fit as a function of range # in each case, we fix the parameter at the value # then we let the optimisation go while holding it fixed - + # we now set the base ranges - fig1=plt.figure() - plt.xlabel('Relative variation') - plt.ylabel('log-likelihood') - + fig1 = plt.figure() + plt.xlabel("Relative variation") + plt.ylabel("log-likelihood") + ### Emax ### - which=1 - rels=np.linspace(-1,1,3) - Emaxes=pset[1]*10**rels - delta=0.1 - lls1,psets1=one_parameter_error_range(grids,surveys,pset,which,delta,crit=0.5) #0.5 is about 1 sigma - opdir=outdir+it.get_names(1)+'/' + which = 1 + rels = np.linspace(-1, 1, 3) + Emaxes = pset[1] * 10 ** rels + delta = 0.1 + lls1, psets1 = one_parameter_error_range( + grids, surveys, pset, which, delta, crit=0.5 + ) # 0.5 is about 1 sigma + opdir = outdir + it.get_names(1) + "/" if not os.path.exists(opdir): os.mkdir(opdir) - savename=opdir+'correlation_'+it.get_lnames(1)+'.pdf' - do_correlation_plots(Emaxes,lls1,psets1,[0,1],savename) # tells it that parameters 0 and 1 are not to be plotted - - plt.plot(rels,lls1,label=it.get_lnames(1)) - - + savename = opdir + "correlation_" + it.get_lnames(1) + ".pdf" + do_correlation_plots( + Emaxes, lls1, psets1, [0, 1], savename + ) # tells it that parameters 0 and 1 are not to be plotted + + plt.plot(rels, lls1, label=it.get_lnames(1)) + plt.tight_layout() - plt.savefig(outdir+'varying_likelihoods.pdf') + plt.savefig(outdir + "varying_likelihoods.pdf") plt.close() - -def do_correlation_plots(vals,lls,psets,const,savename): + +def do_correlation_plots(vals, lls, psets, const, savename): """ Plots correlations of different variables """ - + plt.figure() - nv,np=psets.shape() + nv, np = psets.shape() for i in np.arange(np): if i in const: continue - plt.plot(vals,psets[:,i],label=it.get_lnames(i)) + plt.plot(vals, psets[:, i], label=it.get_lnames(i)) plt.savefig(savename) - -def one_parameter_error_range(grids,surveys,pset,which,delta,crit=0.5): + + +def one_parameter_error_range(grids, surveys, pset, which, delta, crit=0.5): """ Investigates a range of errors for each parameter in 1D only which is which parameter to investigate rels are the list of relative """ - + # keep original pset - tpset=np.copy(pset) - #lls=np.zeros([values.size]) - #sets=np.zeros([values.size,pset.size]) - - lls=[] - vals=[] - psets=[pset] + tpset = np.copy(pset) + # lls=np.zeros([values.size]) + # sets=np.zeros([values.size,pset.size]) + + lls = [] + vals = [] + psets = [pset] print("About to minimise...") - ll,ps=it.my_minimise(tpset,grids,surveys,disable=[0,which]) - lls=[ll] - psets=[ps] - ll0=ll - llcrit=ll0-crit - vals=[tpset[which]] - - print("Found initial minimum at ",ll,ps) - - tpset[3]=0.1 - + ll, ps = it.my_minimise(tpset, grids, surveys, disable=[0, which]) + lls = [ll] + psets = [ps] + ll0 = ll + llcrit = ll0 - crit + vals = [tpset[which]] + + print("Found initial minimum at ", ll, ps) + + tpset[3] = 0.1 + # goes down - while(ll > llcrit): + while ll > llcrit: tpset[which] -= delta - t0=time.process_time() - ll,ps=it.my_minimise(tpset,grids,surveys,disable=[0,which]) - t1=time.process_time() - lls.insert(0,ll) - psets.insert(0,ps) - vals.insert(0,tpset[which]) - print("In time ",t1-t0," values now ",ll,ps) - if ll==0.: - break # means parameter are meaningless - + t0 = time.process_time() + ll, ps = it.my_minimise(tpset, grids, surveys, disable=[0, which]) + t1 = time.process_time() + lls.insert(0, ll) + psets.insert(0, ps) + vals.insert(0, tpset[which]) + print("In time ", t1 - t0, " values now ", ll, ps) + if ll == 0.0: + break # means parameter are meaningless + # resets tpset = pset - ll=ll0 - + ll = ll0 + # goes up - while(ll > llcrit): + while ll > llcrit: tpset[which] += delta - t0=time.process_time() - ll,ps=it.my_minimise(tpset,grids,surveys,disable=[0,which]) - t1=time.process_time() + t0 = time.process_time() + ll, ps = it.my_minimise(tpset, grids, surveys, disable=[0, which]) + t1 = time.process_time() lls.append(ll) psets.append(ps) vals.append(tpset[which]) - print("In time ",t1-t0," values now ",ll,ps) - if ll==0.: - break # means we got an nan and parameters are meaningless - - return lls,psets - -def get_zgdm_priors(grid,survey,savename): + print("In time ", t1 - t0, " values now ", ll, ps) + if ll == 0.0: + break # means we got an nan and parameters are meaningless + + return lls, psets + + +def get_zgdm_priors(grid, survey, savename): """ Plots priors as a function of redshift for each FRB in the survey Likely outdated, should use the likelihoods function. """ - priors=grid.get_p_zgdm(survey.DMEGs) + priors = grid.get_p_zgdm(survey.DMEGs) plt.figure() - plt.xlabel('$z$') - plt.ylabel('$p(z|{\\rm DM})$') - for i,dm in enumerate(survey.DMs): - if i<10: - style="-" + plt.xlabel("$z$") + plt.ylabel("$p(z|{\\rm DM})$") + for i, dm in enumerate(survey.DMs): + if i < 10: + style = "-" else: - style=":" - plt.plot(grid.zvals,priors[i,:],label=str(dm),linestyle=style) - plt.xlim(0,0.5) - plt.legend(fontsize=8,ncol=2) + style = ":" + plt.plot(grid.zvals, priors[i, :], label=str(dm), linestyle=style) + plt.xlim(0, 0.5) + plt.legend(fontsize=8, ncol=2) plt.tight_layout() plt.savefig(savename) plt.close() -def make_dm_redshift(grid,savename="",DMmax=1000, - zmax=1,loc='upper left',Macquart=None, - H0=None,showplot=False): - ''' generates full dm-redhsift (Macquart) relation ''' + +def make_dm_redshift( + grid, + savename="", + DMmax=1000, + zmax=1, + loc="upper left", + Macquart=None, + H0=None, + showplot=False, +): + """ generates full dm-redhsift (Macquart) relation """ if H0 is None: H0 = cos.cosmo.H0 - ndm=1000 - cvs=[0.025,0.16,0.5,0.84,0.975] - nc=len(cvs) - names=['$2\\sigma$','$1\\sigma$','Median','',''] - styles=[':','--','-','--',':'] - colours=["white","white","black","white","white"] - DMs=np.linspace(DMmax/ndm,DMmax,ndm,endpoint=True) - priors=grid.get_p_zgdm(DMs) - zvals=grid.zvals - means=np.mean(priors,axis=1) - csums=np.cumsum(priors,axis=1) - - crits=np.zeros([nc,ndm]) - + ndm = 1000 + cvs = [0.025, 0.16, 0.5, 0.84, 0.975] + nc = len(cvs) + names = ["$2\\sigma$", "$1\\sigma$", "Median", "", ""] + styles = [":", "--", "-", "--", ":"] + colours = ["white", "white", "black", "white", "white"] + DMs = np.linspace(DMmax / ndm, DMmax, ndm, endpoint=True) + priors = grid.get_p_zgdm(DMs) + zvals = grid.zvals + means = np.mean(priors, axis=1) + csums = np.cumsum(priors, axis=1) + + crits = np.zeros([nc, ndm]) + for i in np.arange(ndm): - for j,c in enumerate(cvs): - ic=np.where(csums[i]>c)[0][0] - if ic>0: - kc=(csums[i,ic]-c)/(csums[i,ic]-csums[i,ic-1]) - crits[j,i]=zvals[ic]*(1-kc)+zvals[ic-1]*kc + for j, c in enumerate(cvs): + ic = np.where(csums[i] > c)[0][0] + if ic > 0: + kc = (csums[i, ic] - c) / (csums[i, ic] - csums[i, ic - 1]) + crits[j, i] = zvals[ic] * (1 - kc) + zvals[ic - 1] * kc else: - crits[j,i]=zvals[ic] - + crits[j, i] = zvals[ic] + # now we convert this between real values and integer units - dz=zvals[1]-zvals[0] + dz = zvals[1] - zvals[0] crits /= dz - + ### concatenate for plotting ### - delete=np.where(zvals > zmax)[0][0] - plotpriors=priors[:,0:delete] - plotz=zvals[0:delete] - + delete = np.where(zvals > zmax)[0][0] + plotpriors = priors[:, 0:delete] + plotz = zvals[0:delete] + plt.figure() - + ############# sets the x and y tics ################3 - ytvals=np.arange(plotz.size) - every=int(plotz.size/5) - ytickpos=np.insert(ytvals[every-1::every],[0],[0]) - yticks=np.insert(plotz[every-1::every],[0],[0]) - - #plt.yticks(ytvals[every-1::every],plotz[every-1::every]) - plt.yticks(ytickpos,yticks) - xtvals=np.arange(ndm) - everx=int(ndm/5) - xtickpos=np.insert(xtvals[everx-1::everx],[0],[0]) - xticks=np.insert(DMs[everx-1::everx],[0],[0]) - plt.xticks(xtickpos,xticks) - #plt.xticks(xtvals[everx-1::everx],DMs[everx-1::everx]) - - ax=plt.gca() + ytvals = np.arange(plotz.size) + every = int(plotz.size / 5) + ytickpos = np.insert(ytvals[every - 1 :: every], [0], [0]) + yticks = np.insert(plotz[every - 1 :: every], [0], [0]) + + # plt.yticks(ytvals[every-1::every],plotz[every-1::every]) + plt.yticks(ytickpos, yticks) + xtvals = np.arange(ndm) + everx = int(ndm / 5) + xtickpos = np.insert(xtvals[everx - 1 :: everx], [0], [0]) + xticks = np.insert(DMs[everx - 1 :: everx], [0], [0]) + plt.xticks(xtickpos, xticks) + # plt.xticks(xtvals[everx-1::everx],DMs[everx-1::everx]) + + ax = plt.gca() labels = [item.get_text() for item in ax.get_xticklabels()] for i in np.arange(len(labels)): - thisl=len(labels[i]) - labels[i]=labels[i][0:thisl-1] + thisl = len(labels[i]) + labels[i] = labels[i][0 : thisl - 1] ax.set_xticklabels(labels) - + #### rescales priors to max value for visibility's sake #### - dm_max=np.max(plotpriors,axis=1) + dm_max = np.max(plotpriors, axis=1) for i in np.arange(ndm): - plotpriors[i,:] /= np.max(plotpriors[i,:]) - - - - cmx = plt.get_cmap('cubehelix') - plt.xlabel('${\\rm DM}_{\\rm EG}$') - plt.ylabel('z') - - aspect=float(ndm)/plotz.size - plt.imshow(plotpriors.T,origin='lower',cmap=cmx,aspect=aspect) - cbar=plt.colorbar() - cbar.set_label('$p(z|{\\rm DM})/p_{\\rm max}(z|{\\rm DM})$') + plotpriors[i, :] /= np.max(plotpriors[i, :]) + + cmx = plt.get_cmap("cubehelix") + plt.xlabel("${\\rm DM}_{\\rm EG}$") + plt.ylabel("z") + + aspect = float(ndm) / plotz.size + plt.imshow(plotpriors.T, origin="lower", cmap=cmx, aspect=aspect) + cbar = plt.colorbar() + cbar.set_label("$p(z|{\\rm DM})/p_{\\rm max}(z|{\\rm DM})$") ###### now we plot the specific thingamies ####### - for i,c in enumerate(cvs): - plt.plot(np.arange(ndm),crits[i,:],linestyle=styles[i],label=names[i],color=colours[i]) - - #Macquart=None + for i, c in enumerate(cvs): + plt.plot( + np.arange(ndm), + crits[i, :], + linestyle=styles[i], + label=names[i], + color=colours[i], + ) + + # Macquart=None if Macquart is not None: - plt.ylim(0,ytvals.size) - nz=zvals.size - - plt.xlim(0,xtvals.size) - zmax=zvals[-1] - DMbar, zeval = igm.average_DM(zmax, cumul=True, neval=nz+1) - DMbar = DMbar*H0/(cos.DEF_H0) # NOT SURE THIS IS RIGHT - DMbar=np.array(DMbar) - DMbar += Macquart #should be interpreted as muDM - - - #idea is that 1 point is 1, hence... - zeval /= (zvals[1]-zvals[0]) - DMbar /= (DMs[1]-DMs[0]) - - plt.plot(DMbar,zeval,linewidth=2,label='Macquart',color='blue') - #l=plt.legend(loc='lower right',fontsize=12) - #l=plt.legend(bbox_to_anchor=(0.2, 0.8),fontsize=8) - #for text in l.get_texts(): - # text.set_color("white") - - #plt.plot([30,40],[0.5,10],linewidth=10) - + plt.ylim(0, ytvals.size) + nz = zvals.size + + plt.xlim(0, xtvals.size) + zmax = zvals[-1] + DMbar, zeval = igm.average_DM(zmax, cumul=True, neval=nz + 1) + DMbar = DMbar * H0 / (cos.DEF_H0) # NOT SURE THIS IS RIGHT + DMbar = np.array(DMbar) + DMbar += Macquart # should be interpreted as muDM + + # idea is that 1 point is 1, hence... + zeval /= zvals[1] - zvals[0] + DMbar /= DMs[1] - DMs[0] + + plt.plot(DMbar, zeval, linewidth=2, label="Macquart", color="blue") + # l=plt.legend(loc='lower right',fontsize=12) + # l=plt.legend(bbox_to_anchor=(0.2, 0.8),fontsize=8) + # for text in l.get_texts(): + # text.set_color("white") + + # plt.plot([30,40],[0.5,10],linewidth=10) + plt.legend(loc=loc) plt.savefig(savename) if H0 is not None: @@ -415,155 +460,170 @@ def make_dm_redshift(grid,savename="",DMmax=1000, plt.close() +def fit_width_test(pset, surveys, grids, names): + x0 = [2.139, 0.997] + x0 = [1.7712265624999926, 0.9284453124999991] + args = [pset, surveys, grids, names] + result = min_wt(x0, args) + print("result of fit is ", result) -def fit_width_test(pset,surveys,grids,names): - x0=[2.139,0.997] - x0=[1.7712265624999926, 0.9284453124999991] - args=[pset,surveys,grids,names] - result=min_wt(x0,args) - print("result of fit is ",result) - -def min_wt(x,args): - - logmean=x[0] - logsigma=x[1] - - pset=args[0] - surveys=args[1] - grids=args[2] - names=args[3] - - oldchi2=1e10 - dlogmean=0.1 - dlogsigma=0.1 - +def min_wt(x, args): + + logmean = x[0] + logsigma = x[1] + + pset = args[0] + surveys = args[1] + grids = args[2] + names = args[3] + + oldchi2 = 1e10 + dlogmean = 0.1 + dlogsigma = 0.1 + for i in np.arange(10): - - while(True): + + while True: logmean -= dlogmean - W,C=basic_width_test(pset,[surveys[0]],[grids[0]],logmean,logsigma) - chi2=np.sum((W-C)**2) + W, C = basic_width_test(pset, [surveys[0]], [grids[0]], logmean, logsigma) + chi2 = np.sum((W - C) ** 2) if chi2 > oldchi2: logmean += dlogmean break else: - oldchi2=chi2 - while(True): + oldchi2 = chi2 + while True: logmean += dlogmean - W,C=basic_width_test(pset,[surveys[0]],[grids[0]],logmean,logsigma) - chi2=np.sum((W-C)**2) + W, C = basic_width_test(pset, [surveys[0]], [grids[0]], logmean, logsigma) + chi2 = np.sum((W - C) ** 2) if chi2 > oldchi2: logmean -= dlogmean break else: - oldchi2=chi2 - while(True): + oldchi2 = chi2 + while True: logsigma += dlogsigma - W,C=basic_width_test(pset,[surveys[0]],[grids[0]],logmean,logsigma) - chi2=np.sum((W-C)**2) + W, C = basic_width_test(pset, [surveys[0]], [grids[0]], logmean, logsigma) + chi2 = np.sum((W - C) ** 2) if chi2 > oldchi2: logsigma -= dlogsigma break else: - oldchi2=chi2 - while(True): + oldchi2 = chi2 + while True: logsigma -= dlogsigma - W,C=basic_width_test(pset,[surveys[0]],[grids[0]],logmean,logsigma) - chi2=np.sum((W-C)**2) + W, C = basic_width_test(pset, [surveys[0]], [grids[0]], logmean, logsigma) + chi2 = np.sum((W - C) ** 2) if chi2 > oldchi2: logsigma += dlogsigma break else: - oldchi2=chi2 - dlogsigma /= 2. - dlogmean /= 2. - print(i,logmean,logsigma,chi2) - return logmean,logsigma,chi2 + oldchi2 = chi2 + dlogsigma /= 2.0 + dlogmean /= 2.0 + print(i, logmean, logsigma, chi2) + return logmean, logsigma, chi2 -def basic_width_test(pset,surveys,grids,logmean=2,logsigma=1): +def basic_width_test(pset, surveys, grids, logmean=2, logsigma=1): """ Tests the effects of intrinsic widths on FRB properties """ - - IGNORE=0. # a parameter that gets ignored - + + IGNORE = 0.0 # a parameter that gets ignored + ############ set default parameters for width distribution ########### # 'real' version - wmin=0.1 - wmax=20 - NW=200 - #short version - #wmin=0.1 - #wmax=50 - #NW=100 - dw=(wmax-wmin)/2. - widths=np.linspace(wmin,wmax,NW) - - probs=pcosmic.linlognormal_dlin(widths,[logmean,logsigma]) #not integrating, just amplitudes + wmin = 0.1 + wmax = 20 + NW = 200 + # short version + # wmin=0.1 + # wmax=50 + # NW=100 + dw = (wmax - wmin) / 2.0 + widths = np.linspace(wmin, wmax, NW) + + probs = pcosmic.linlognormal_dlin( + widths, [logmean, logsigma] + ) # not integrating, just amplitudes # normalise probabilities probs /= np.sum(probs) - - MAX=1 - norm=MAX/np.max(probs) + + MAX = 1 + norm = MAX / np.max(probs) probs *= norm - - Emin=10**pset[0] - Emax=10**pset[1] - alpha=pset[2] - gamma=pset[3] - NS=len(surveys) - rates=np.zeros([NS,NW]) - rp=np.zeros([NS,NW]) - - #calculating total rate compared to what is expected for ~width 0 - sumw=0. - DMvals=grids[0].dmvals - zvals=grids[0].zvals - - dmplots=np.zeros([NS,NW,DMvals.size]) - zplots=np.zeros([NS,NW,zvals.size]) - + + Emin = 10 ** pset[0] + Emax = 10 ** pset[1] + alpha = pset[2] + gamma = pset[3] + NS = len(surveys) + rates = np.zeros([NS, NW]) + rp = np.zeros([NS, NW]) + + # calculating total rate compared to what is expected for ~width 0 + sumw = 0.0 + DMvals = grids[0].dmvals + zvals = grids[0].zvals + + dmplots = np.zeros([NS, NW, DMvals.size]) + zplots = np.zeros([NS, NW, zvals.size]) + #### does this for width distributions ####### - - for i,s in enumerate(surveys): - g=grids[i] - fbar=s.meta['FBAR'] - tres=s.meta['TRES'] - fres=s.meta['FRES'] - thresh=s.meta['THRESH'] + + for i, s in enumerate(surveys): + g = grids[i] + fbar = s.meta["FBAR"] + tres = s.meta["TRES"] + fres = s.meta["FRES"] + thresh = s.meta["THRESH"] ###### "Full" version ###### - for j,w in enumerate(widths): + for j, w in enumerate(widths): # artificially set response function - sens=survey.calc_relative_sensitivity(IGNORE,DMvals,w,fbar,tres,fres,model='Quadrature',dsmear=False) - - g.calc_thresholds(thresh,sens,alpha=alpha) - g.calc_pdv(Emin,Emax,gamma) - + sens = survey.calc_relative_sensitivity( + IGNORE, DMvals, w, fbar, tres, fres, model="Quadrature", dsmear=False + ) + + g.calc_thresholds(thresh, sens, alpha=alpha) + g.calc_pdv(Emin, Emax, gamma) + g.calc_rates() - rates[i,j]=np.sum(g.rates) - dmplots[i,j,:]=np.sum(g.rates,axis=0) - zplots[i,j,:]=np.sum(g.rates,axis=1) - - rates[i,:] /= rates[i,0] - - #norm_orates[i,:] = orates[i,0]/rates[i,0] - rp[i,:]=rates[i,:]*probs - - rp[i,:]*=MAX/np.max(rp[i,:]) - + rates[i, j] = np.sum(g.rates) + dmplots[i, j, :] = np.sum(g.rates, axis=0) + zplots[i, j, :] = np.sum(g.rates, axis=1) + + rates[i, :] /= rates[i, 0] + + # norm_orates[i,:] = orates[i,0]/rates[i,0] + rp[i, :] = rates[i, :] * probs + + rp[i, :] *= MAX / np.max(rp[i, :]) + # shows the distribution of widths using Wayne's method - WAlogmean=np.log(2.67) - WAlogsigma=np.log(2.07) - - waprobs=pcosmic.linlognormal_dlin(widths,[WAlogmean,WAlogsigma]) - wasum=np.sum(waprobs) - - #rename - WAprobs = waprobs*MAX/np.max(waprobs) - - return WAprobs,rp[0,:] #for lat50 + WAlogmean = np.log(2.67) + WAlogsigma = np.log(2.07) -def width_test(pset,surveys,grids,names,logmean=2,logsigma=1,plot=True,outdir='Plots/',NP=5,scale=3.5): + waprobs = pcosmic.linlognormal_dlin(widths, [WAlogmean, WAlogsigma]) + wasum = np.sum(waprobs) + + # rename + WAprobs = waprobs * MAX / np.max(waprobs) + + return WAprobs, rp[0, :] # for lat50 + + +def width_test( + pset, + surveys, + grids, + names, + logmean=2, + logsigma=1, + plot=True, + outdir="Plots/", + NP=5, + scale=3.5, +): """ Tests the effects of intrinsic widths on FRB properties Considers three cases: - width distribution of Wayne Arcus et al (2020) @@ -573,969 +633,1286 @@ def width_test(pset,surveys,grids,names,logmean=2,logsigma=1,plot=True,outdir='P """ - + if plot: print("Performing test of intrinsic width effects") - t0=time.process_time() - - IGNORE=0. # a parameter that gets ignored - + t0 = time.process_time() + + IGNORE = 0.0 # a parameter that gets ignored + ############ set default parameters for width distribution ########### # 'real' version - wmin=0.1 - wmax=30 - NW=300 - #short version - #wmin=0.1 - #wmax=50 - #NW=100 - dw=(wmax-wmin)/2. - widths=np.linspace(wmin,wmax,NW) - - probs=pcosmic.linlognormal_dlin(widths,logmean,logsigma) #not integrating, just amplitudes + wmin = 0.1 + wmax = 30 + NW = 300 + # short version + # wmin=0.1 + # wmax=50 + # NW=100 + dw = (wmax - wmin) / 2.0 + widths = np.linspace(wmin, wmax, NW) + + probs = pcosmic.linlognormal_dlin( + widths, logmean, logsigma + ) # not integrating, just amplitudes # normalise probabilities probs /= np.sum(probs) - pextra,err=sp.integrate.quad(pcosmic.loglognormal_dlog,np.log(wmax+dw/2.),np.log(wmax*2),args=(logmean,logsigma)) - probs *= (1.-pextra) # now sums to 1.-pextra - probs[-1] += pextra # now sums back to 1 - - styles=['--','-.',':'] - - MAX=1 - norm=MAX/np.max(probs[:-1]) + pextra, err = sp.integrate.quad( + pcosmic.loglognormal_dlog, + np.log(wmax + dw / 2.0), + np.log(wmax * 2), + args=(logmean, logsigma), + ) + probs *= 1.0 - pextra # now sums to 1.-pextra + probs[-1] += pextra # now sums back to 1 + + styles = ["--", "-.", ":"] + + MAX = 1 + norm = MAX / np.max(probs[:-1]) probs *= norm - wsum=np.sum(probs) + wsum = np.sum(probs) if plot: plt.figure() - plt.xlabel('w [ms]') - plt.ylabel('p(w)') - plt.xlim(0,wmax) - plt.plot(widths[:-1],probs[:-1],label='This work: $\\mu_w=5.49, \\sigma_w=2.46$',linewidth=2) - - Emin=10**pset[0] - Emax=10**pset[1] - alpha=pset[2] - gamma=pset[3] - NS=len(surveys) - rates=np.zeros([NS,NW]) - rp=np.zeros([NS,NW]) - warp=np.zeros([NS,NW]) - #loop over surveys - #colours=['blue','orange',' - - names=['ASKAP/FE','ASKAP/ICS','Parkes/MB'] - colours=['blue','red','green','orange','black'] - - #calculating total rate compared to what is expected for ~width 0 - sumw=0. - DMvals=grids[0].dmvals - zvals=grids[0].zvals - - dmplots=np.zeros([NS,NW,DMvals.size]) - zplots=np.zeros([NS,NW,zvals.size]) - + plt.xlabel("w [ms]") + plt.ylabel("p(w)") + plt.xlim(0, wmax) + plt.plot( + widths[:-1], + probs[:-1], + label="This work: $\\mu_w=5.49, \\sigma_w=2.46$", + linewidth=2, + ) + + Emin = 10 ** pset[0] + Emax = 10 ** pset[1] + alpha = pset[2] + gamma = pset[3] + NS = len(surveys) + rates = np.zeros([NS, NW]) + rp = np.zeros([NS, NW]) + warp = np.zeros([NS, NW]) + # loop over surveys + # colours=['blue','orange',' + + names = ["ASKAP/FE", "ASKAP/ICS", "Parkes/MB"] + colours = ["blue", "red", "green", "orange", "black"] + + # calculating total rate compared to what is expected for ~width 0 + sumw = 0.0 + DMvals = grids[0].dmvals + zvals = grids[0].zvals + + dmplots = np.zeros([NS, NW, DMvals.size]) + zplots = np.zeros([NS, NW, zvals.size]) + ##### values for 'practical' arrays ##### - - #NP=5 #NP 10 at scale 2 good - #scale=3.5 - - pdmplots=np.zeros([NS,NP,DMvals.size]) - pzplots=np.zeros([NS,NP,zvals.size]) - prates=np.zeros([NS,NP]) - + + # NP=5 #NP 10 at scale 2 good + # scale=3.5 + + pdmplots = np.zeros([NS, NP, DMvals.size]) + pzplots = np.zeros([NS, NP, zvals.size]) + prates = np.zeros([NS, NP]) + # collapsed over width dimension with appropriate weights - spdmplots=np.zeros([NS,DMvals.size]) - spzplots=np.zeros([NS,zvals.size]) - sprates=np.zeros([NS]) - + spdmplots = np.zeros([NS, DMvals.size]) + spzplots = np.zeros([NS, zvals.size]) + sprates = np.zeros([NS]) + ######## gets original rates for DM and z distributions ######### - #norm_orates=([NS,zvals.size,DMvals.size) # normed to width=0! + # norm_orates=([NS,zvals.size,DMvals.size) # normed to width=0! # wait - does this include beamshape and the others not? - orates=np.zeros([NS]) - norates=np.zeros([NS]) #for normed version - odms=np.zeros([NS,DMvals.size]) - ozs=np.zeros([NS,zvals.size]) - for i,g in enumerate(grids): - odms[i,:]=np.sum(g.rates,axis=0) - ozs[i,:]=np.sum(g.rates,axis=1) - orates[i]=np.sum(g.rates) #total rate for grid - 'original' rates - + orates = np.zeros([NS]) + norates = np.zeros([NS]) # for normed version + odms = np.zeros([NS, DMvals.size]) + ozs = np.zeros([NS, zvals.size]) + for i, g in enumerate(grids): + odms[i, :] = np.sum(g.rates, axis=0) + ozs[i, :] = np.sum(g.rates, axis=1) + orates[i] = np.sum(g.rates) # total rate for grid - 'original' rates + ############ Wayne Arcus's fits ##########3 # calculates probabilities and uses this later; WAprobs - WAlogmean=np.log(2.67) - WAlogsigma=np.log(2.07) - waprobs=pcosmic.linlognormal_dlin(widths,WAlogmean,WAlogsigma) + WAlogmean = np.log(2.67) + WAlogsigma = np.log(2.07) + waprobs = pcosmic.linlognormal_dlin(widths, WAlogmean, WAlogsigma) waprobs /= np.sum(waprobs) - pextra,err=sp.integrate.quad(pcosmic.loglognormal_dlog,np.log(wmax+dw/2.),np.log(wmax*2),args=(WAlogmean,WAlogsigma)) - waprobs *= (1.-pextra) # now sums to 1.-pextra - waprobs[-1] += pextra # now sums back to 1 - wasum=np.sum(waprobs) - - #rename - WAprobs = waprobs*MAX/np.max(waprobs) - WAsum=np.sum(WAprobs) - #print(np.max(rates[0,:]),np.max(WAprobs)) - ls=['-','--',':','-.','-.'] - - - + pextra, err = sp.integrate.quad( + pcosmic.loglognormal_dlog, + np.log(wmax + dw / 2.0), + np.log(wmax * 2), + args=(WAlogmean, WAlogsigma), + ) + waprobs *= 1.0 - pextra # now sums to 1.-pextra + waprobs[-1] += pextra # now sums back to 1 + wasum = np.sum(waprobs) + + # rename + WAprobs = waprobs * MAX / np.max(waprobs) + WAsum = np.sum(WAprobs) + # print(np.max(rates[0,:]),np.max(WAprobs)) + ls = ["-", "--", ":", "-.", "-."] + #### does this for width distributions ####### - - for i,s in enumerate(surveys): - g=grids[i] - #DMvals=grids[i].dmvals - + + for i, s in enumerate(surveys): + g = grids[i] + # DMvals=grids[i].dmvals + # gets the 'practical' widths for this survey - pwidths,pprobs=survey.make_widths(s,g.state) - + pwidths, pprobs = survey.make_widths(s, g.state) + pnorm_probs = pprobs / np.max(pprobs) - - #if plot: + + # if plot: # plt.plot(pwidths,pnorm_probs,color=colours[i],marker='o',linestyle='',label='Approx.') # gets the survey parameters - fbar=s.meta['FBAR'] - tres=s.meta['TRES'] - fres=s.meta['FRES'] - thresh=s.meta['THRESH'] - - + fbar = s.meta["FBAR"] + tres = s.meta["TRES"] + fres = s.meta["FRES"] + thresh = s.meta["THRESH"] + ######## "practical" version ### (note: not using default behaviour) ######## - for j,w in enumerate(pwidths): + for j, w in enumerate(pwidths): # artificially set response function - sens=survey.calc_relative_sensitivity(IGNORE,DMvals,w,fbar,tres,fres,model='Quadrature',dsmear=False) - g.calc_thresholds(thresh,sens,alpha=alpha) - g.calc_pdv(Emin,Emax,gamma) - + sens = survey.calc_relative_sensitivity( + IGNORE, DMvals, w, fbar, tres, fres, model="Quadrature", dsmear=False + ) + g.calc_thresholds(thresh, sens, alpha=alpha) + g.calc_pdv(Emin, Emax, gamma) + g.calc_rates() - prates[i,j]=np.sum(g.rates)*pprobs[j] - pdmplots[i,j,:]=np.sum(g.rates,axis=0)*pprobs[j] - pzplots[i,j,:]=np.sum(g.rates,axis=1)*pprobs[j] - #sum over weights - could just do all this later, but whatever - sprates[i]=np.sum(prates[i],axis=0) - spdmplots[i]=np.sum(pdmplots[i],axis=0) - spzplots[i]=np.sum(pzplots[i],axis=0) - + prates[i, j] = np.sum(g.rates) * pprobs[j] + pdmplots[i, j, :] = np.sum(g.rates, axis=0) * pprobs[j] + pzplots[i, j, :] = np.sum(g.rates, axis=1) * pprobs[j] + # sum over weights - could just do all this later, but whatever + sprates[i] = np.sum(prates[i], axis=0) + spdmplots[i] = np.sum(pdmplots[i], axis=0) + spzplots[i] = np.sum(pzplots[i], axis=0) + ######### "Full" (correct) version ######### - for j,w in enumerate(widths): + for j, w in enumerate(widths): # artificially set response function - sens=survey.calc_relative_sensitivity(IGNORE,DMvals,w,fbar,tres,fres,model='Quadrature',dsmear=False) - - g.calc_thresholds(thresh,sens,alpha=alpha) - g.calc_pdv(Emin,Emax,gamma) - + sens = survey.calc_relative_sensitivity( + IGNORE, DMvals, w, fbar, tres, fres, model="Quadrature", dsmear=False + ) + + g.calc_thresholds(thresh, sens, alpha=alpha) + g.calc_pdv(Emin, Emax, gamma) + g.calc_rates() - rates[i,j]=np.sum(g.rates) - - dmplots[i,j,:]=np.sum(g.rates,axis=0) - zplots[i,j,:]=np.sum(g.rates,axis=1) - + rates[i, j] = np.sum(g.rates) + + dmplots[i, j, :] = np.sum(g.rates, axis=0) + zplots[i, j, :] = np.sum(g.rates, axis=1) + # this step divides by the full rates for zero width - norates[i]=orates[i]/rates[i,0] #normalises original weight by rate if no width - sprates[i] /= rates[i,0] - rates[i,:] /= rates[i,0] - - #norm_orates[i,:] = orates[i,0]/rates[i,0] - rp[i,:]=rates[i,:]*probs - warp[i,:]=rates[i,:]*WAprobs - + norates[i] = ( + orates[i] / rates[i, 0] + ) # normalises original weight by rate if no width + sprates[i] /= rates[i, 0] + rates[i, :] /= rates[i, 0] + + # norm_orates[i,:] = orates[i,0]/rates[i,0] + rp[i, :] = rates[i, :] * probs + warp[i, :] = rates[i, :] * WAprobs + if plot: - plt.plot(widths[:-1],rp[i,:-1],linestyle=styles[i],linewidth=1) - - norm=MAX/np.max(rp[i,:-1]) + plt.plot(widths[:-1], rp[i, :-1], linestyle=styles[i], linewidth=1) + + norm = MAX / np.max(rp[i, :-1]) if plot: - plt.plot(widths[:-1],rp[i,:-1]*norm,label=names[i],linestyle=styles[i],color=plt.gca().lines[-1].get_color(),linewidth=2) - + plt.plot( + widths[:-1], + rp[i, :-1] * norm, + label=names[i], + linestyle=styles[i], + color=plt.gca().lines[-1].get_color(), + linewidth=2, + ) + print("The total fraction of events detected as a function of experiment are") print("Survey name [input_grid] WA lognormal practical") - for i,s in enumerate(surveys): - print(i,names[i],norates[i],np.sum(warp[i,:])/WAsum,np.sum(rp[i,:])/wsum,sprates[i]) - #print(i,rates[i,:]) - #print(i,names[i],np.sum(rates[i,:]),np.sum(rp[i,:]),wsum,np.sum(rp[i,:])/wsum) - - - + for i, s in enumerate(surveys): + print( + i, + names[i], + norates[i], + np.sum(warp[i, :]) / WAsum, + np.sum(rp[i, :]) / wsum, + sprates[i], + ) + # print(i,rates[i,:]) + # print(i,names[i],np.sum(rates[i,:]),np.sum(rp[i,:]),wsum,np.sum(rp[i,:])/wsum) + if plot: - plt.plot(widths[:-1],WAprobs[:-1],label='Arcus et al: $\\mu_w=2.67, \\sigma_w=2.07$',color='black',linestyle='-',linewidth=2) - - plt.legend(loc='upper right') + plt.plot( + widths[:-1], + WAprobs[:-1], + label="Arcus et al: $\\mu_w=2.67, \\sigma_w=2.07$", + color="black", + linestyle="-", + linewidth=2, + ) + + plt.legend(loc="upper right") plt.tight_layout() - plt.xlim(0,30) - plt.savefig(outdir+'/width_effect.pdf') + plt.xlim(0, 30) + plt.savefig(outdir + "/width_effect.pdf") plt.close() - t1=time.process_time() - print("Done. Took ",t1-t0," seconds.") - - + t1 = time.process_time() + print("Done. Took ", t1 - t0, " seconds.") + #### we now do DM plots ### plt.figure() - plt.xlabel('DM [pc cm$^{-3}$]') - plt.ylabel('p(DM) [a.u.]') - plt.xlim(0,3000) - #dmplots[i,j,:]=np.sum(g.rates,axis=0) - - twdm=np.zeros([NS,DMvals.size]) - wadm=np.zeros([NS,DMvals.size]) - w0dm=np.zeros([NS,DMvals.size]) - for i,s in enumerate(surveys): - - w0dm[i]=dmplots[i,0,:] - wadm[i]=np.sum((waprobs.T*dmplots[i,:,:].T).T,axis=0)/wasum - twdm[i]=np.sum((probs.T*dmplots[i,:,:].T).T,axis=0)/wsum - - - print("Mean DM for survey ",i," is (0) ",np.sum(DMvals*w0dm[i,:])/np.sum(w0dm[i,:])) - print(" (full verson) ",np.sum(DMvals*twdm[i,:])/np.sum(twdm[i,:])) - print(" (wayne arcus a) ",np.sum(DMvals*wadm[i,:])/np.sum(wadm[i,:])) - print(" (practical) ",np.sum(DMvals*spdmplots[i,:])/np.sum(spdmplots[i,:])) - - #plt.plot(DMvals,w0dm[i]/np.max(w0dm[i]),label=names[i],linewidth=0.1) - #plt.plot(DMvals,twdm[i]/np.max(twdm[i]),color=plt.gca().lines[-1].get_color(),linestyle='--') - #plt.plot(DMvals,wadm[i]/np.max(wadm[i]),color=plt.gca().lines[-1].get_color(),linestyle='-.') - #plt.plot(DMvals,odms[i]/np.max(odms[i]),color=plt.gca().lines[-1].get_color(),linestyle=':') - #plt.plot(DMvals,spdmplots[i]/np.max(spdmplots[i]),color=plt.gca().lines[-1].get_color(),linestyle=':') - if i==0: - plt.plot(DMvals,w0dm[i]/np.max(w0dm[i]),linestyle=ls[0],label='$w_{\\rm inc}=0$',color=colours[0]) - #plt.plot(DMvals,wadm[i]/np.max(wadm[i]),linestyle=ls[2],label='Arcus et al: $\\mu_w=2.67, \\sigma_w=2.07$',color=colours[2]) - #plt.plot(DMvals,twdm[i]/np.max(twdm[i]),linestyle=ls[1],label='This work: $\\mu_w=5.49, \\sigma_w=2.46$',color=colours[1]) - plt.plot(DMvals,wadm[i]/np.max(wadm[i]),linestyle=ls[2],label='Arcus et al.',color=colours[2]) - plt.plot(DMvals,twdm[i]/np.max(twdm[i]),linestyle=ls[1],label='This work',color=colours[1]) - - #plt.plot(DMvals,odms[i]/np.max(odms[i]),linestyle=ls[3],label='old',color=colours[3]) - plt.plot(DMvals,spdmplots[i]/np.max(spdmplots[i]),linestyle=ls[4],label='This work',color=colours[4]) + plt.xlabel("DM [pc cm$^{-3}$]") + plt.ylabel("p(DM) [a.u.]") + plt.xlim(0, 3000) + # dmplots[i,j,:]=np.sum(g.rates,axis=0) + + twdm = np.zeros([NS, DMvals.size]) + wadm = np.zeros([NS, DMvals.size]) + w0dm = np.zeros([NS, DMvals.size]) + for i, s in enumerate(surveys): + + w0dm[i] = dmplots[i, 0, :] + wadm[i] = np.sum((waprobs.T * dmplots[i, :, :].T).T, axis=0) / wasum + twdm[i] = np.sum((probs.T * dmplots[i, :, :].T).T, axis=0) / wsum + + print( + "Mean DM for survey ", + i, + " is (0) ", + np.sum(DMvals * w0dm[i, :]) / np.sum(w0dm[i, :]), + ) + print( + " (full verson) ", + np.sum(DMvals * twdm[i, :]) / np.sum(twdm[i, :]), + ) + print( + " (wayne arcus a) ", + np.sum(DMvals * wadm[i, :]) / np.sum(wadm[i, :]), + ) + print( + " (practical) ", + np.sum(DMvals * spdmplots[i, :]) / np.sum(spdmplots[i, :]), + ) + + # plt.plot(DMvals,w0dm[i]/np.max(w0dm[i]),label=names[i],linewidth=0.1) + # plt.plot(DMvals,twdm[i]/np.max(twdm[i]),color=plt.gca().lines[-1].get_color(),linestyle='--') + # plt.plot(DMvals,wadm[i]/np.max(wadm[i]),color=plt.gca().lines[-1].get_color(),linestyle='-.') + # plt.plot(DMvals,odms[i]/np.max(odms[i]),color=plt.gca().lines[-1].get_color(),linestyle=':') + # plt.plot(DMvals,spdmplots[i]/np.max(spdmplots[i]),color=plt.gca().lines[-1].get_color(),linestyle=':') + if i == 0: + plt.plot( + DMvals, + w0dm[i] / np.max(w0dm[i]), + linestyle=ls[0], + label="$w_{\\rm inc}=0$", + color=colours[0], + ) + # plt.plot(DMvals,wadm[i]/np.max(wadm[i]),linestyle=ls[2],label='Arcus et al: $\\mu_w=2.67, \\sigma_w=2.07$',color=colours[2]) + # plt.plot(DMvals,twdm[i]/np.max(twdm[i]),linestyle=ls[1],label='This work: $\\mu_w=5.49, \\sigma_w=2.46$',color=colours[1]) + plt.plot( + DMvals, + wadm[i] / np.max(wadm[i]), + linestyle=ls[2], + label="Arcus et al.", + color=colours[2], + ) + plt.plot( + DMvals, + twdm[i] / np.max(twdm[i]), + linestyle=ls[1], + label="This work", + color=colours[1], + ) + + # plt.plot(DMvals,odms[i]/np.max(odms[i]),linestyle=ls[3],label='old',color=colours[3]) + plt.plot( + DMvals, + spdmplots[i] / np.max(spdmplots[i]), + linestyle=ls[4], + label="This work", + color=colours[4], + ) else: - plt.plot(DMvals,w0dm[i]/np.max(w0dm[i]),linestyle=ls[0],color=colours[0]) - plt.plot(DMvals,twdm[i]/np.max(twdm[i]),linestyle=ls[1],color=colours[1]) - plt.plot(DMvals,wadm[i]/np.max(wadm[i]),linestyle=ls[2],color=colours[2]) - #plt.plot(DMvals,odms[i]/np.max(odms[i]),linestyle=ls[3],color=colours[3]) - plt.plot(DMvals,spdmplots[i]/np.max(spdmplots[i]),linestyle=ls[4],color=colours[4]) - plt.legend(loc='upper right') + plt.plot( + DMvals, w0dm[i] / np.max(w0dm[i]), linestyle=ls[0], color=colours[0] + ) + plt.plot( + DMvals, twdm[i] / np.max(twdm[i]), linestyle=ls[1], color=colours[1] + ) + plt.plot( + DMvals, wadm[i] / np.max(wadm[i]), linestyle=ls[2], color=colours[2] + ) + # plt.plot(DMvals,odms[i]/np.max(odms[i]),linestyle=ls[3],color=colours[3]) + plt.plot( + DMvals, + spdmplots[i] / np.max(spdmplots[i]), + linestyle=ls[4], + color=colours[4], + ) + plt.legend(loc="upper right") plt.tight_layout() - plt.savefig(outdir+'/width_dm_effect.pdf') + plt.savefig(outdir + "/width_dm_effect.pdf") plt.close() - + ##### z plots #### plt.figure() - plt.xlabel('z') - plt.ylabel('p(z) [a.u.]') - plt.xlim(0,3) - #zplots[i,j,:]=np.sum(g.rates,axis=1) - - twz=np.zeros([NS,zvals.size]) - waz=np.zeros([NS,zvals.size]) - w0z=np.zeros([NS,zvals.size]) - for i,s in enumerate(surveys): - - w0z[i]=zplots[i,0,:] - waz[i]=np.sum((waprobs.T*zplots[i,:,:].T).T,axis=0)/wasum - twz[i]=np.sum((probs.T*zplots[i,:,:].T).T,axis=0)/wsum - - print("Mean z for survey ",i," is (0) ",np.sum(zvals*w0z[i])/np.sum(w0z[i])) - print(" (tw) ",np.sum(zvals*twz[i])/np.sum(twz[i])) - print(" (wa) ",np.sum(zvals*waz[i])/np.sum(waz[i])) - print(" (p) ",np.sum(zvals*spzplots[i,:])/np.sum(spzplots[i,:])) - - #plt.plot(zvals,w0z[i]/np.max(w0z[i]),label=names[i]) - #plt.plot(zvals,twz[i]/np.max(twz[i]),color=plt.gca().lines[-1].get_color(),linestyle='--') - #plt.plot(zvals,waz[i]/np.max(waz[i]),color=plt.gca().lines[-1].get_color(),linestyle='-.') - #plt.plot(zvals,ozs[i]/np.max(ozs[i]),color=plt.gca().lines[-1].get_color(),linestyle=':') - #plt.plot(zvals,spzplots[i]/np.max(spzplots[i]),color=plt.gca().lines[-1].get_color(),linestyle=':') - if i==0: - plt.plot(zvals,w0z[i]/np.max(w0z[i]),label='$w_{\\rm inc}=0$',linestyle=ls[0],color=colours[0]) - #plt.plot(zvals,waz[i]/np.max(waz[i]),linestyle=ls[2],label='Arcus et al: $\\mu_w=2.67, \\sigma_w=2.07$',color=colours[2]) - #plt.plot(zvals,twz[i]/np.max(twz[i]),label='This work: $\\mu_w=5.49, \\sigma_w=2.46$',linestyle=ls[1],color=colours[1]) - plt.plot(zvals,waz[i]/np.max(waz[i]),linestyle=ls[2],label='Arcus et al.',color=colours[2]) - plt.plot(zvals,twz[i]/np.max(twz[i]),label='This work',linestyle=ls[1],color=colours[1]) - #plt.plot(zvals,ozs[i]/np.max(ozs[i]),linestyle=ls[3],label='orig',color=colours[3]) - plt.plot(zvals,spzplots[i]/np.max(spzplots[i]),linestyle=ls[4],label='This work',color=colours[4]) + plt.xlabel("z") + plt.ylabel("p(z) [a.u.]") + plt.xlim(0, 3) + # zplots[i,j,:]=np.sum(g.rates,axis=1) + + twz = np.zeros([NS, zvals.size]) + waz = np.zeros([NS, zvals.size]) + w0z = np.zeros([NS, zvals.size]) + for i, s in enumerate(surveys): + + w0z[i] = zplots[i, 0, :] + waz[i] = np.sum((waprobs.T * zplots[i, :, :].T).T, axis=0) / wasum + twz[i] = np.sum((probs.T * zplots[i, :, :].T).T, axis=0) / wsum + + print( + "Mean z for survey ", + i, + " is (0) ", + np.sum(zvals * w0z[i]) / np.sum(w0z[i]), + ) + print( + " (tw) ", + np.sum(zvals * twz[i]) / np.sum(twz[i]), + ) + print( + " (wa) ", + np.sum(zvals * waz[i]) / np.sum(waz[i]), + ) + print( + " (p) ", + np.sum(zvals * spzplots[i, :]) / np.sum(spzplots[i, :]), + ) + + # plt.plot(zvals,w0z[i]/np.max(w0z[i]),label=names[i]) + # plt.plot(zvals,twz[i]/np.max(twz[i]),color=plt.gca().lines[-1].get_color(),linestyle='--') + # plt.plot(zvals,waz[i]/np.max(waz[i]),color=plt.gca().lines[-1].get_color(),linestyle='-.') + # plt.plot(zvals,ozs[i]/np.max(ozs[i]),color=plt.gca().lines[-1].get_color(),linestyle=':') + # plt.plot(zvals,spzplots[i]/np.max(spzplots[i]),color=plt.gca().lines[-1].get_color(),linestyle=':') + if i == 0: + plt.plot( + zvals, + w0z[i] / np.max(w0z[i]), + label="$w_{\\rm inc}=0$", + linestyle=ls[0], + color=colours[0], + ) + # plt.plot(zvals,waz[i]/np.max(waz[i]),linestyle=ls[2],label='Arcus et al: $\\mu_w=2.67, \\sigma_w=2.07$',color=colours[2]) + # plt.plot(zvals,twz[i]/np.max(twz[i]),label='This work: $\\mu_w=5.49, \\sigma_w=2.46$',linestyle=ls[1],color=colours[1]) + plt.plot( + zvals, + waz[i] / np.max(waz[i]), + linestyle=ls[2], + label="Arcus et al.", + color=colours[2], + ) + plt.plot( + zvals, + twz[i] / np.max(twz[i]), + label="This work", + linestyle=ls[1], + color=colours[1], + ) + # plt.plot(zvals,ozs[i]/np.max(ozs[i]),linestyle=ls[3],label='orig',color=colours[3]) + plt.plot( + zvals, + spzplots[i] / np.max(spzplots[i]), + linestyle=ls[4], + label="This work", + color=colours[4], + ) else: - plt.plot(zvals,w0z[i]/np.max(w0z[i]),linestyle=ls[0],color=colours[0]) - plt.plot(zvals,twz[i]/np.max(twz[i]),linestyle=ls[1],color=colours[1]) - plt.plot(zvals,waz[i]/np.max(waz[i]),linestyle=ls[2],color=colours[2]) - #plt.plot(zvals,ozs[i]/np.max(ozs[i]),linestyle=ls[3],color=colours[3]) - plt.plot(zvals,spzplots[i]/np.max(spzplots[i]),linestyle=ls[4],color=colours[4]) - plt.legend(loc='upper right') + plt.plot( + zvals, w0z[i] / np.max(w0z[i]), linestyle=ls[0], color=colours[0] + ) + plt.plot( + zvals, twz[i] / np.max(twz[i]), linestyle=ls[1], color=colours[1] + ) + plt.plot( + zvals, waz[i] / np.max(waz[i]), linestyle=ls[2], color=colours[2] + ) + # plt.plot(zvals,ozs[i]/np.max(ozs[i]),linestyle=ls[3],color=colours[3]) + plt.plot( + zvals, + spzplots[i] / np.max(spzplots[i]), + linestyle=ls[4], + color=colours[4], + ) + plt.legend(loc="upper right") plt.tight_layout() - plt.savefig(outdir+'/width_z_effect.pdf') + plt.savefig(outdir + "/width_z_effect.pdf") plt.close() - - return WAprobs,rp[0,:] #for lat50 -def test_pks_beam(surveys,zDMgrid, zvals,dmvals,pset,outdir='Plots/BeamTest/',zmax=1,DMmax=1000): - + return WAprobs, rp[0, :] # for lat50 + + +def test_pks_beam( + surveys, zDMgrid, zvals, dmvals, pset, outdir="Plots/BeamTest/", zmax=1, DMmax=1000 +): + if not os.path.isdir(outdir): os.mkdir(outdir) - + # get parameter values - lEmin,lEmax,alpha,gamma,sfr_n,logmean,logsigma=pset - Emin=10**lEmin - Emax=10**lEmax - + lEmin, lEmax, alpha, gamma, sfr_n, logmean, logsigma = pset + Emin = 10 ** lEmin + Emax = 10 ** lEmax + # generates a DM mask # creates a mask of values in DM space to convolve with the DM grid - mask=pcosmic.get_dm_mask(dmvals,(logmean,logsigma),zvals,plot=True) - + mask = pcosmic.get_dm_mask(dmvals, (logmean, logsigma), zvals, plot=True) + # get an initial grid with no beam values - grids=[] - bbs=[] - bos=[] - - + grids = [] + bbs = [] + bos = [] + print("Just got into test parkes beam") - - - #norms=np.zeros([len(surveys)]) - #numbins=np.zeros([len(surveys)]) - rates=[] - New=False - for i,s in enumerate(surveys): - print("Starting",i) - #s.beam_b - #s.beam_o - print("Sum of i is ",np.sum(s.beam_o)) + + # norms=np.zeros([len(surveys)]) + # numbins=np.zeros([len(surveys)]) + rates = [] + New = False + for i, s in enumerate(surveys): + print("Starting", i) + # s.beam_b + # s.beam_o + print("Sum of i is ", np.sum(s.beam_o)) print(s.beam_o) print(s.beam_b) - if New==True: - - grid=zdm_grid.Grid() - grid.pass_grid(zDMgrid,zvals,dmvals) - grid.smear_dm(mask,logmean,logsigma) - efficiencies=s.get_efficiency(dmvals) - grid.calc_thresholds(s.meta['THRESH'],s.mean_efficiencies,alpha=alpha) + if New == True: + + grid = zdm_grid.Grid() + grid.pass_grid(zDMgrid, zvals, dmvals) + grid.smear_dm(mask, logmean, logsigma) + efficiencies = s.get_efficiency(dmvals) + grid.calc_thresholds(s.meta["THRESH"], s.mean_efficiencies, alpha=alpha) grid.calc_dV() - grid.set_evolution(sfr_n) # sets star-formation rate scaling with z - here, no evoltion... - grid.calc_pdv(Emin,Emax,gamma,s.beam_b,s.beam_o) # calculates volumetric-weighted probabilities - grid.calc_rates() # calculates rates by multiplying above with pdm plot - name=outdir+'rates_'+s.meta["BEAM"]+'.pdf' - plot_grid_2(grid.rates,grid.zvals,grid.dmvals,zmax=zmax,DMmax=DMmax,name=name,norm=2,log=True,label='$f(DM,z)p(DM,z)dV$ [Mpc$^3$]',project=True) + grid.set_evolution( + sfr_n + ) # sets star-formation rate scaling with z - here, no evoltion... + grid.calc_pdv( + Emin, Emax, gamma, s.beam_b, s.beam_o + ) # calculates volumetric-weighted probabilities + grid.calc_rates() # calculates rates by multiplying above with pdm plot + name = outdir + "rates_" + s.meta["BEAM"] + ".pdf" + plot_grid_2( + grid.rates, + grid.zvals, + grid.dmvals, + zmax=zmax, + DMmax=DMmax, + name=name, + norm=2, + log=True, + label="$f(DM,z)p(DM,z)dV$ [Mpc$^3$]", + project=True, + ) grids.append(grid) - np.save(outdir+s.meta["BEAM"]+'_rates.npy',grid.rates) - rate=grid.rates + np.save(outdir + s.meta["BEAM"] + "_rates.npy", grid.rates) + rate = grid.rates else: - rate=np.load(outdir+s.meta["BEAM"]+'_rates.npy') - print("Sum of rates: ",np.sum(rate),s.meta["BEAM"]) + rate = np.load(outdir + s.meta["BEAM"] + "_rates.npy") + print("Sum of rates: ", np.sum(rate), s.meta["BEAM"]) rates.append(rate) - fig1=plt.figure() - plt.xlabel('z') - plt.xlim(0,zmax) - fig2=plt.figure() - plt.xlabel('DM') - plt.xlim(0,DMmax) - - fig3=plt.figure() - plt.xlabel('z') - plt.xlim(0,zmax) - fig4=plt.figure() - plt.xlabel('DM') - plt.xlim(0,DMmax) - - #plt.yscale('log') + fig1 = plt.figure() + plt.xlabel("z") + plt.xlim(0, zmax) + fig2 = plt.figure() + plt.xlabel("DM") + plt.xlim(0, DMmax) + + fig3 = plt.figure() + plt.xlabel("z") + plt.xlim(0, zmax) + fig4 = plt.figure() + plt.xlabel("DM") + plt.xlim(0, DMmax) + + # plt.yscale('log') # now does z-only and dm-only projection plots for Parkes - for i,s in enumerate(surveys): - r=rates[i] - z=np.sum(r,axis=1) - dm=np.sum(r,axis=0) + for i, s in enumerate(surveys): + r = rates[i] + z = np.sum(r, axis=1) + dm = np.sum(r, axis=0) plt.figure(fig1.number) - plt.plot(zvals,z,label=s.meta["BEAM"]) + plt.plot(zvals, z, label=s.meta["BEAM"]) plt.figure(fig2.number) - plt.plot(dmvals,dm,label=s.meta["BEAM"]) - + plt.plot(dmvals, dm, label=s.meta["BEAM"]) + z /= np.sum(z) dm /= np.sum(dm) plt.figure(fig3.number) - plt.plot(zvals,z,label=s.meta["BEAM"]) + plt.plot(zvals, z, label=s.meta["BEAM"]) plt.figure(fig4.number) - plt.plot(dmvals,dm,label=s.meta["BEAM"]) - + plt.plot(dmvals, dm, label=s.meta["BEAM"]) + plt.figure(fig1.number) - plt.legend(loc='upper right') + plt.legend(loc="upper right") plt.tight_layout() - plt.savefig(outdir+'z_projections.pdf') + plt.savefig(outdir + "z_projections.pdf") plt.close() - + plt.figure(fig2.number) - plt.legend(loc='upper right') + plt.legend(loc="upper right") plt.tight_layout() - plt.savefig(outdir+'dm_projections.pdf') + plt.savefig(outdir + "dm_projections.pdf") plt.close() - + plt.figure(fig3.number) - plt.legend(loc='upper right') + plt.legend(loc="upper right") plt.tight_layout() - plt.savefig(outdir+'normed_z_projections.pdf') + plt.savefig(outdir + "normed_z_projections.pdf") plt.close() - + plt.figure(fig4.number) - plt.legend(loc='upper right') + plt.legend(loc="upper right") plt.tight_layout() - plt.savefig(outdir+'normed_dm_projections.pdf') + plt.savefig(outdir + "normed_dm_projections.pdf") plt.close() - + ###### makes a 1d set of plots in dm and redshift ######## - font = {'family' : 'normal', - 'weight' : 'normal', - 'size' : 6} + font = {"family": "normal", "weight": "normal", "size": 6} + + matplotlib.rc("font", **font) + fig, ax = plt.subplots( + 3, 2, sharey="row", sharex="col" + ) # ,sharey='row',sharex='col') + + ax[1, 0].set_xlabel("z") + ax[1, 1].set_xlabel("DM") + ax[2, 0].set_xlabel("z") + ax[2, 1].set_xlabel("DM") + ax[0, 0].set_ylabel("Abs") + ax[0, 1].set_ylabel("Abs") + ax[1, 0].set_ylabel("Dabs") + ax[1, 1].set_ylabel("Dabs") + ax[2, 0].set_ylabel("Rel diff") + ax[2, 1].set_ylabel("Rel diff") - matplotlib.rc('font', **font) - fig,ax=plt.subplots(3,2,sharey='row',sharex='col')#,sharey='row',sharex='col') - - ax[1,0].set_xlabel('z') - ax[1,1].set_xlabel('DM') - ax[2,0].set_xlabel('z') - ax[2,1].set_xlabel('DM') - ax[0,0].set_ylabel('Abs') - ax[0,1].set_ylabel('Abs') - ax[1,0].set_ylabel('Dabs') - ax[1,1].set_ylabel('Dabs') - ax[2,0].set_ylabel('Rel diff') - ax[2,1].set_ylabel('Rel diff') - # force relative range only - ax[2,0].set_ylim(-1,1) - ax[2,1].set_ylim(-1,1) - - ax[0,0].set_xlim(0,zmax) - ax[0,1].set_xlim(0,DMmax) - ax[1,0].set_xlim(0,zmax) - ax[2,0].set_xlim(0,zmax) - ax[1,1].set_xlim(0,DMmax) - ax[2,1].set_xlim(0,DMmax) - + ax[2, 0].set_ylim(-1, 1) + ax[2, 1].set_ylim(-1, 1) + + ax[0, 0].set_xlim(0, zmax) + ax[0, 1].set_xlim(0, DMmax) + ax[1, 0].set_xlim(0, zmax) + ax[2, 0].set_xlim(0, zmax) + ax[1, 1].set_xlim(0, DMmax) + ax[2, 1].set_xlim(0, DMmax) + # gets Keith's normalised rates - kr=rates[2] - kz=np.sum(kr,axis=1) - kdm=np.sum(kr,axis=0) + kr = rates[2] + kz = np.sum(kr, axis=1) + kdm = np.sum(kr, axis=0) kz /= np.sum(kz) kdm /= np.sum(kdm) - - ax[0,0].plot(zvals,kz,label=surveys[2].meta["BEAM"],color='black') - ax[0,1].plot(dmvals,kdm,label=surveys[2].meta["BEAM"],color='black') - - for i,s in enumerate(surveys): - if i==2: + + ax[0, 0].plot(zvals, kz, label=surveys[2].meta["BEAM"], color="black") + ax[0, 1].plot(dmvals, kdm, label=surveys[2].meta["BEAM"], color="black") + + for i, s in enumerate(surveys): + if i == 2: continue - - #calculates relative and absolute errors in dm and z space - z=np.sum(rates[i],axis=1) - dm=np.sum(rates[i],axis=0) + + # calculates relative and absolute errors in dm and z space + z = np.sum(rates[i], axis=1) + dm = np.sum(rates[i], axis=0) z /= np.sum(z) dm /= np.sum(dm) - - dz=z-kz - ddm = dm-kdm - rdz=dz/kz - rdm=ddm/kdm - - ax[0,0].plot(zvals,z,label=s.meta["BEAM"]) - ax[0,1].plot(dmvals,dm,label=s.meta["BEAM"]) - ax[1,0].plot(zvals,dz,label=s.meta["BEAM"]) - ax[1,1].plot(dmvals,ddm,label=s.meta["BEAM"]) - ax[2,0].plot(zvals,rdz,label=s.meta["BEAM"]) - ax[2,1].plot(dmvals,rdm,label=s.meta["BEAM"]) - ax[0,0].legend(fontsize=6) + + dz = z - kz + ddm = dm - kdm + rdz = dz / kz + rdm = ddm / kdm + + ax[0, 0].plot(zvals, z, label=s.meta["BEAM"]) + ax[0, 1].plot(dmvals, dm, label=s.meta["BEAM"]) + ax[1, 0].plot(zvals, dz, label=s.meta["BEAM"]) + ax[1, 1].plot(dmvals, ddm, label=s.meta["BEAM"]) + ax[2, 0].plot(zvals, rdz, label=s.meta["BEAM"]) + ax[2, 1].plot(dmvals, rdm, label=s.meta["BEAM"]) + ax[0, 0].legend(fontsize=6) plt.figure(fig.number) plt.tight_layout() - plt.savefig(outdir+'montage.pdf') + plt.savefig(outdir + "montage.pdf") plt.close() -def final_plot_beam_rates(surveys,zDMgrid, zvals,dmvals,pset,binset,names,logsigma,logmean,outdir,LOAD=True): + +def final_plot_beam_rates( + surveys, + zDMgrid, + zvals, + dmvals, + pset, + binset, + names, + logsigma, + logmean, + outdir, + LOAD=True, +): """ For each survey, compare 'full' calculation to 'relative' in dm and z space binset is one for each survey, to be compared to 'all' """ - - #need new ones for new grid shape - + + # need new ones for new grid shape + # hard-coded best values - method=2 - thresh=0 - - + method = 2 + thresh = 0 + ###### makes a 1d set of plots in dm and redshift ######## - font = {'family' : 'normal', - 'weight' : 'normal', - 'size' : 10} + font = {"family": "normal", "weight": "normal", "size": 10} + + matplotlib.rc("font", **font) - matplotlib.rc('font', **font) - - # get parameter values - lEmin,lEmax,alpha,gamma,sfr_n,logmean,logsigma,C=pset - Emin=10**lEmin - Emax=10**lEmax - + lEmin, lEmax, alpha, gamma, sfr_n, logmean, logsigma, C = pset + Emin = 10 ** lEmin + Emax = 10 ** lEmax + # generates a DM mask # creates a mask of values in DM space to convolve with the DM grid - mask=pcosmic.get_dm_mask(dmvals,(logmean,logsigma),zvals) - - f1, (ax11, ax12) = plt.subplots(2, 1, gridspec_kw={'height_ratios': [3, 1]},sharex=True) - + mask = pcosmic.get_dm_mask(dmvals, (logmean, logsigma), zvals) + + f1, (ax11, ax12) = plt.subplots( + 2, 1, gridspec_kw={"height_ratios": [3, 1]}, sharex=True + ) + plt.subplots_adjust(wspace=0, hspace=0) - ax11.set_xlim(0,2) - ax12.set_xlim(0,2) - ax11.set_ylabel('$p(z)$ [a.u.]') - ax12.set_ylabel('$p_{\\rm Full}(z)-p(z)$') - ax12.set_xlabel('z') - - f2, (ax21, ax22) = plt.subplots(2, 1, gridspec_kw={'height_ratios': [3, 1]},sharex=True) + ax11.set_xlim(0, 2) + ax12.set_xlim(0, 2) + ax11.set_ylabel("$p(z)$ [a.u.]") + ax12.set_ylabel("$p_{\\rm Full}(z)-p(z)$") + ax12.set_xlabel("z") + + f2, (ax21, ax22) = plt.subplots( + 2, 1, gridspec_kw={"height_ratios": [3, 1]}, sharex=True + ) plt.subplots_adjust(wspace=0, hspace=0) - ax21.set_xlim(0,2500) - ax22.set_xlim(0,2500) - ax21.set_ylabel('$p(\\rm DM_{\\rm EG})$ [a.u.]') - ax22.set_ylabel('$p(\\rm DM_{\\rm EG})-p_{\\rm Full}(\\rm DM_{\\rm EG})$') - ax22.set_xlabel('${\\rm DM}_{\\rm EG}$') - + ax21.set_xlim(0, 2500) + ax22.set_xlim(0, 2500) + ax21.set_ylabel("$p(\\rm DM_{\\rm EG})$ [a.u.]") + ax22.set_ylabel("$p(\\rm DM_{\\rm EG})-p_{\\rm Full}(\\rm DM_{\\rm EG})$") + ax22.set_xlabel("${\\rm DM}_{\\rm EG}$") + # does this for each survey # for lat50, FE, and Parkes - FWHM0=np.array([32,32,0.54])*(np.pi/180)**2 # nominal deg square + FWHM0 = np.array([32, 32, 0.54]) * (np.pi / 180) ** 2 # nominal deg square print("Generating plots illustrating the effect of beamshape") print("Order is FWHM, Numerical, Gaussian") - for i,s in enumerate(surveys): - #efficiencies=s.get_efficiency(dmvals) - efficiencies=s.efficiencies # two dimensions - weights=s.wplist - + for i, s in enumerate(surveys): + # efficiencies=s.get_efficiency(dmvals) + efficiencies = s.efficiencies # two dimensions + weights = s.wplist + ######## Naive FWHM case - single beam value, single angle ######## - + if LOAD: - rates=np.load('TEMP/'+str(i)+'1.npy') - + rates = np.load("TEMP/" + str(i) + "1.npy") + else: - t0=t0=time.process_time() + t0 = t0 = time.process_time() # set up grid, which should be common for this survey - grid=zdm_grid.Grid() - grid.pass_grid(zDMgrid,zvals,dmvals) - grid.smear_dm(mask,logmean,logsigma) - grid.calc_thresholds(s.meta['THRESH'],efficiencies,alpha=alpha,weights=weights) + grid = zdm_grid.Grid() + grid.pass_grid(zDMgrid, zvals, dmvals) + grid.smear_dm(mask, logmean, logsigma) + grid.calc_thresholds( + s.meta["THRESH"], efficiencies, alpha=alpha, weights=weights + ) grid.calc_dV() - grid.set_evolution(sfr_n) # sets star-formation rate scaling with z - here, no evoltion... - grid.b_fractions=None # trick! - grid.calc_pdv(Emin,Emax,gamma,np.array([1]),np.array([FWHM0[i]])) # calculates volumetric-weighted probabilities - grid.calc_rates() # calculates rates by multiplying above with pdm plot - t1=time.process_time() - np.save('TEMP/'+str(i)+'1.npy',grid.rates) - rates=grid.rates - - total1=np.sum(rates) - rates1=rates / total1 - - fz1=np.sum(rates1,axis=1) - fdm1=np.sum(rates1,axis=0) - + grid.set_evolution( + sfr_n + ) # sets star-formation rate scaling with z - here, no evoltion... + grid.b_fractions = None # trick! + grid.calc_pdv( + Emin, Emax, gamma, np.array([1]), np.array([FWHM0[i]]) + ) # calculates volumetric-weighted probabilities + grid.calc_rates() # calculates rates by multiplying above with pdm plot + t1 = time.process_time() + np.save("TEMP/" + str(i) + "1.npy", grid.rates) + rates = grid.rates + + total1 = np.sum(rates) + rates1 = rates / total1 + + fz1 = np.sum(rates1, axis=1) + fdm1 = np.sum(rates1, axis=0) + ######## full case - very detailed! ######## if LOAD: - rates=np.load('TEMP/'+str(i)+'2.npy') + rates = np.load("TEMP/" + str(i) + "2.npy") else: - s.init_beam(nbins=1,method=3,thresh=thresh) #nbins ignored for method=3 - #s.init_beam(nbins=1,method=2,thresh=thresh) #make it fast! - - grid.b_fractions=None # trick! - grid.calc_pdv(Emin,Emax,gamma,s.beam_b,s.beam_o) # calculates volumetric-weighted probabilities - grid.calc_rates() # calculates rates by multiplying above with pdm plot - np.save('TEMP/'+str(i)+'2.npy',grid.rates) - rates=grid.rates - total2=np.sum(rates) - rates2=rates / total2 - fz2=np.sum(rates2,axis=1) - fdm2=np.sum(rates2,axis=0) - + s.init_beam(nbins=1, method=3, thresh=thresh) # nbins ignored for method=3 + # s.init_beam(nbins=1,method=2,thresh=thresh) #make it fast! + + grid.b_fractions = None # trick! + grid.calc_pdv( + Emin, Emax, gamma, s.beam_b, s.beam_o + ) # calculates volumetric-weighted probabilities + grid.calc_rates() # calculates rates by multiplying above with pdm plot + np.save("TEMP/" + str(i) + "2.npy", grid.rates) + rates = grid.rates + total2 = np.sum(rates) + rates2 = rates / total2 + fz2 = np.sum(rates2, axis=1) + fdm2 = np.sum(rates2, axis=0) + ######## Case of nbins bins - 'standard' ######### if LOAD: - rates=np.load('TEMP/'+str(i)+'3.npy') + rates = np.load("TEMP/" + str(i) + "3.npy") else: - - s.init_beam(nbins=binset[i],method=method,thresh=thresh) - grid.b_fractions=None # trick! - grid.calc_pdv(Emin,Emax,gamma,s.beam_b,s.beam_o) # calculates volumetric-weighted probabilities - grid.calc_rates() # calculates rates by multiplying above with pdm plot - np.save('TEMP/'+str(i)+'3.npy',grid.rates) - rates=grid.rates - total3=np.sum(rates) - rates3=rates / total3 - fz3=np.sum(rates3,axis=1) - fdm3=np.sum(rates3,axis=0) - + + s.init_beam(nbins=binset[i], method=method, thresh=thresh) + grid.b_fractions = None # trick! + grid.calc_pdv( + Emin, Emax, gamma, s.beam_b, s.beam_o + ) # calculates volumetric-weighted probabilities + grid.calc_rates() # calculates rates by multiplying above with pdm plot + np.save("TEMP/" + str(i) + "3.npy", grid.rates) + rates = grid.rates + total3 = np.sum(rates) + rates3 = rates / total3 + fz3 = np.sum(rates3, axis=1) + fdm3 = np.sum(rates3, axis=0) + ######## Gaussian case ######### - + if LOAD: - rates=np.load('TEMP/'+str(i)+'4.npy') + rates = np.load("TEMP/" + str(i) + "4.npy") else: - - thresh=1e-3 #argh! - s.init_beam(nbins=100,method=method,thresh=thresh,Gauss=True) - - grid.b_fractions=None # trick! - grid.calc_pdv(Emin,Emax,gamma,s.beam_b,s.beam_o) # calculates volumetric-weighted probabilities - grid.calc_rates() # calculates rates by multiplying above with pdm plot - np.save('TEMP/'+str(i)+'4.npy',grid.rates) - rates=grid.rates - total4=np.sum(rates) - rates4=rates / total4 - fz4=np.sum(rates4,axis=1) - fdm4=np.sum(rates4,axis=0) - - + + thresh = 1e-3 # argh! + s.init_beam(nbins=100, method=method, thresh=thresh, Gauss=True) + + grid.b_fractions = None # trick! + grid.calc_pdv( + Emin, Emax, gamma, s.beam_b, s.beam_o + ) # calculates volumetric-weighted probabilities + grid.calc_rates() # calculates rates by multiplying above with pdm plot + np.save("TEMP/" + str(i) + "4.npy", grid.rates) + rates = grid.rates + total4 = np.sum(rates) + rates4 = rates / total4 + fz4 = np.sum(rates4, axis=1) + fdm4 = np.sum(rates4, axis=0) + ######## calculate some statistics ######### - + # stats for redshift z - - true_mean=np.sum(fz2*zvals) - dm_mean=np.sum(fdm2*dmvals) - - nerr1=total1/total2 - nerr3=total3/total2 - nerr4=total4/total2 - zerr1=np.sum(fz1*zvals)/true_mean - zerr3=np.sum(fz3*zvals)/true_mean - zerr4=np.sum(fz4*zvals)/true_mean - dmerr1=np.sum(fdm1*dmvals)/dm_mean - dmerr3=np.sum(fdm3*dmvals)/dm_mean - dmerr4=np.sum(fdm4*dmvals)/dm_mean - - print("\n\n\nNormalisation errors: ",nerr1,nerr3,nerr4) - print("zerr : ",zerr1,zerr3,zerr4) - print("dmerr : ",dmerr1,dmerr3,dmerr4) - + + true_mean = np.sum(fz2 * zvals) + dm_mean = np.sum(fdm2 * dmvals) + + nerr1 = total1 / total2 + nerr3 = total3 / total2 + nerr4 = total4 / total2 + zerr1 = np.sum(fz1 * zvals) / true_mean + zerr3 = np.sum(fz3 * zvals) / true_mean + zerr4 = np.sum(fz4 * zvals) / true_mean + dmerr1 = np.sum(fdm1 * dmvals) / dm_mean + dmerr3 = np.sum(fdm3 * dmvals) / dm_mean + dmerr4 = np.sum(fdm4 * dmvals) / dm_mean + + print("\n\n\nNormalisation errors: ", nerr1, nerr3, nerr4) + print("zerr : ", zerr1, zerr3, zerr4) + print("dmerr : ", dmerr1, dmerr3, dmerr4) + ############## plotting ########## # normalise by amplitude of 'true': - normz=np.max(fz2) - normdm=np.max(fdm2) - + normz = np.max(fz2) + normdm = np.max(fdm2) + fz1 /= normz fz2 /= normz fz3 /= normz fz4 /= normz - + fdm1 /= normdm fdm2 /= normdm fdm3 /= normdm fdm4 /= normdm - + plt.sca(ax11) - plt.plot(zvals,fz1,linestyle='--',label=names[i]+' FWHM') - c1=plt.gca().lines[-1].get_color() - + plt.plot(zvals, fz1, linestyle="--", label=names[i] + " FWHM") + c1 = plt.gca().lines[-1].get_color() + plt.sca(ax21) - plt.plot(dmvals,fdm1,linestyle='--',color=c1,label=names[i]+' FWHM') - + plt.plot(dmvals, fdm1, linestyle="--", color=c1, label=names[i] + " FWHM") + plt.sca(ax11) - plt.plot(zvals,fz2,color=c1,linestyle='-',label=' Full beam') - + plt.plot(zvals, fz2, color=c1, linestyle="-", label=" Full beam") + plt.sca(ax21) - plt.plot(dmvals,fdm2,color=c1,linestyle='-',label=' Full beam') - + plt.plot(dmvals, fdm2, color=c1, linestyle="-", label=" Full beam") + plt.sca(ax11) - plt.plot(zvals,fz3,color=plt.gca().lines[-1].get_color(),linestyle=':',label=' This work') - + plt.plot( + zvals, + fz3, + color=plt.gca().lines[-1].get_color(), + linestyle=":", + label=" This work", + ) + plt.sca(ax21) - plt.plot(dmvals,fdm3,color=plt.gca().lines[-1].get_color(),linestyle=':',label=' This work') - + plt.plot( + dmvals, + fdm3, + color=plt.gca().lines[-1].get_color(), + linestyle=":", + label=" This work", + ) + plt.sca(ax11) - plt.plot(zvals,fz4,color=plt.gca().lines[-1].get_color(),linestyle='-.',label=' Gauss') - + plt.plot( + zvals, + fz4, + color=plt.gca().lines[-1].get_color(), + linestyle="-.", + label=" Gauss", + ) + plt.sca(ax21) - plt.plot(dmvals,fdm4,color=plt.gca().lines[-1].get_color(),linestyle='-.',label=' Gauss') - - + plt.plot( + dmvals, + fdm4, + color=plt.gca().lines[-1].get_color(), + linestyle="-.", + label=" Gauss", + ) + ###### now does relative values ####### - dz=fz3-fz2 - ddm=fdm3-fdm2 - - dz0=fz1-fz2 - ddm0=fdm1-fdm2 - - - dzG=fz4-fz2 - ddmG=fdm4-fdm2 - - print("For survey ",i," maximum dz deviation is ",np.max(np.abs(dz0)),np.max(np.abs(dz)),np.max(np.abs(dzG))) - print(" dm deviation is ",np.max(np.abs(ddm0)),np.max(np.abs(ddm)),np.max(np.abs(ddmG))) - + dz = fz3 - fz2 + ddm = fdm3 - fdm2 + + dz0 = fz1 - fz2 + ddm0 = fdm1 - fdm2 + + dzG = fz4 - fz2 + ddmG = fdm4 - fdm2 + + print( + "For survey ", + i, + " maximum dz deviation is ", + np.max(np.abs(dz0)), + np.max(np.abs(dz)), + np.max(np.abs(dzG)), + ) + print( + " dm deviation is ", + np.max(np.abs(ddm0)), + np.max(np.abs(ddm)), + np.max(np.abs(ddmG)), + ) + # plots differences plt.sca(ax12) - ax12.set_ylim(-0.2,0.2) - plt.plot(zvals,dz0,color=c1,linestyle='--') - plt.plot(zvals,dz,color=c1,linestyle=':') - plt.plot(zvals,dzG,color=c1,linestyle='-.') - #ax12.tick_params(axis='y') - - #ax122=ax12.twinx() - #ax122.set_ylim(-0.2,0.2) - - #ax122.tick_params(axis='y') - + ax12.set_ylim(-0.2, 0.2) + plt.plot(zvals, dz0, color=c1, linestyle="--") + plt.plot(zvals, dz, color=c1, linestyle=":") + plt.plot(zvals, dzG, color=c1, linestyle="-.") + # ax12.tick_params(axis='y') + + # ax122=ax12.twinx() + # ax122.set_ylim(-0.2,0.2) + + # ax122.tick_params(axis='y') + plt.sca(ax22) - ax22.set_ylim(-0.2,0.2) - plt.plot(dmvals,ddm0,color=c1,linestyle='--') - plt.plot(dmvals,ddm,color=c1,linestyle=':') - plt.plot(dmvals,ddmG,color=c1,linestyle='-.') - #ax222=ax22.twinx() - #ax222.set_ylim(-0.2,0.2) - - - print("Total rates for are ",i,total1,total2,total3,total4) - + ax22.set_ylim(-0.2, 0.2) + plt.plot(dmvals, ddm0, color=c1, linestyle="--") + plt.plot(dmvals, ddm, color=c1, linestyle=":") + plt.plot(dmvals, ddmG, color=c1, linestyle="-.") + # ax222=ax22.twinx() + # ax222.set_ylim(-0.2,0.2) + + print("Total rates for are ", i, total1, total2, total3, total4) + plt.figure(f1.number) - leg1=ax11.legend(fontsize=8) + leg1 = ax11.legend(fontsize=8) plt.tight_layout() - plt.savefig(outdir+'/beam_z_comp.pdf') + plt.savefig(outdir + "/beam_z_comp.pdf") plt.close() - + plt.figure(f2.number) - leg2=ax21.legend(fontsize=8) + leg2 = ax21.legend(fontsize=8) plt.tight_layout() - plt.savefig(outdir+'/beam_dm_comp.pdf') + plt.savefig(outdir + "/beam_dm_comp.pdf") - -def final_plot_beam_values(surveys,zDMgrid, zvals,dmvals,pset,binset,names,logsigma,logmean,outdir): +def final_plot_beam_values( + surveys, zDMgrid, zvals, dmvals, pset, binset, names, logsigma, logmean, outdir +): """ For each survey, get the beamshape, and plot it vs the dots on the one plot """ # hard-coded best values - method=2 - thresh=0 - + method = 2 + thresh = 0 + # does this for each survey # for lat50, FE, and Parkes - #FWHM0=np.array([32,32,0.54])*(np.pi/180)**2 # nominal deg square - + # FWHM0=np.array([32,32,0.54])*(np.pi/180)**2 # nominal deg square + plt.figure() - plt.yscale('log') - plt.xscale('log') - plt.xlabel('$B$') - plt.ylabel('$\\Omega(B)\\, d\\log_{10}B$ [sr]') # data is dlogB for constant logB - - - markers=['o','o','o'] - lss=["-",":","--"] - names=["ASKAP","ASKAP","Parkes/Mb"] - n=[5,1,1] - for i,s in enumerate(surveys): - if i==1: + plt.yscale("log") + plt.xscale("log") + plt.xlabel("$B$") + plt.ylabel("$\\Omega(B)\\, d\\log_{10}B$ [sr]") # data is dlogB for constant logB + + markers = ["o", "o", "o"] + lss = ["-", ":", "--"] + names = ["ASKAP", "ASKAP", "Parkes/Mb"] + n = [5, 1, 1] + for i, s in enumerate(surveys): + if i == 1: continue - #efficiencies=s.get_efficiency(dmvals) - efficiencies=s.efficiencies # two dimensions - weights=s.wplist - + # efficiencies=s.get_efficiency(dmvals) + efficiencies = s.efficiencies # two dimensions + weights = s.wplist + # gets Gaussian beam - s.init_beam(nbins=binset[i]*10,method=method,thresh=1e-3,Gauss=True) - gb=np.copy(s.beam_b) - go=np.copy(s.beam_o) - gdb=np.log10(gb[0]/gb[1]) - + s.init_beam(nbins=binset[i] * 10, method=method, thresh=1e-3, Gauss=True) + gb = np.copy(s.beam_b) + go = np.copy(s.beam_o) + gdb = np.log10(gb[0] / gb[1]) + # simple point of Nbeams * 1 * FWHM - simple_x=1 - HPBW=1.22*(sp.constants.c/(s.meta["FBAR"]*1e6))/s.meta["DIAM"] - simple_y=np.pi*HPBW**2*s.meta["NBEAMS"] - + simple_x = 1 + HPBW = 1.22 * (sp.constants.c / (s.meta["FBAR"] * 1e6)) / s.meta["DIAM"] + simple_y = np.pi * HPBW ** 2 * s.meta["NBEAMS"] + # standard method - s.init_beam(nbins=binset[i],method=method,thresh=thresh) - - - #calculates normalisation factor: integral B db - orig_db=np.log10(s.orig_beam_b[1]/s.orig_beam_b[0]) + s.init_beam(nbins=binset[i], method=method, thresh=thresh) + + # calculates normalisation factor: integral B db + orig_db = np.log10(s.orig_beam_b[1] / s.orig_beam_b[0]) # log10 grid spacing of original plot # now db is per natural log # first divide by db factor # since d Omega dlogB = d Omega/dB * dB/dlogB = dOmega/dB B # d Omega/dB = d Omega dlogB/B # but we will not do this! - - db=np.log10(s.beam_b[1]/s.beam_b[0]) # also divides this one by the log spacing! - part=np.where(s.orig_beam_b > 1e-3) - to_sqr_deg=(180/np.pi)**2 - #print("The log(10) corrected sums are [deg2]",np.sum(s.orig_beam_o[part])*to_sqr_deg,np.sum(go)/np.log(10)*to_sqr_deg,np.sum(s.beam_o)*to_sqr_deg) - print("The uncorrected sums are [deg2]",np.sum(s.orig_beam_o[part])*to_sqr_deg,np.sum(go)*to_sqr_deg,np.sum(s.beam_o)*to_sqr_deg) - - plt.plot(s.orig_beam_b[::n[i]],s.orig_beam_o[::n[i]]/orig_db,linestyle=lss[i],label=names[i]) - plt.plot(gb,go/gdb,color=plt.gca().lines[-1].get_color(),linestyle=lss[i]) - plt.plot(s.beam_b,s.beam_o/db,marker=markers[i],color=plt.gca().lines[-1].get_color(),linestyle="",markersize=10) - plt.plot(simple_x,simple_y,marker='+',color=plt.gca().lines[-1].get_color(),markersize=10) - + + db = np.log10( + s.beam_b[1] / s.beam_b[0] + ) # also divides this one by the log spacing! + part = np.where(s.orig_beam_b > 1e-3) + to_sqr_deg = (180 / np.pi) ** 2 + # print("The log(10) corrected sums are [deg2]",np.sum(s.orig_beam_o[part])*to_sqr_deg,np.sum(go)/np.log(10)*to_sqr_deg,np.sum(s.beam_o)*to_sqr_deg) + print( + "The uncorrected sums are [deg2]", + np.sum(s.orig_beam_o[part]) * to_sqr_deg, + np.sum(go) * to_sqr_deg, + np.sum(s.beam_o) * to_sqr_deg, + ) + + plt.plot( + s.orig_beam_b[:: n[i]], + s.orig_beam_o[:: n[i]] / orig_db, + linestyle=lss[i], + label=names[i], + ) + plt.plot(gb, go / gdb, color=plt.gca().lines[-1].get_color(), linestyle=lss[i]) + plt.plot( + s.beam_b, + s.beam_o / db, + marker=markers[i], + color=plt.gca().lines[-1].get_color(), + linestyle="", + markersize=10, + ) + plt.plot( + simple_x, + simple_y, + marker="+", + color=plt.gca().lines[-1].get_color(), + markersize=10, + ) + plt.legend() plt.tight_layout() - - plt.savefig(outdir+'/beam_approx.pdf') - -def test_beam_rates(survey,zDMgrid, zvals,dmvals,pset,binset,method=2,outdir='Plots/BeamTest/',thresh=1e-3,zmax=1,DMmax=1000): + plt.savefig(outdir + "/beam_approx.pdf") + + +def test_beam_rates( + survey, + zDMgrid, + zvals, + dmvals, + pset, + binset, + method=2, + outdir="Plots/BeamTest/", + thresh=1e-3, + zmax=1, + DMmax=1000, +): """ For a list of surveys, construct a zDMgrid object binset is the set of bins which we use to simplify the beamset We conclude that method=2, nbeams=5, acc=0 is the best here """ - - #zmax=4 - #DMmax=4000 - + + # zmax=4 + # DMmax=4000 + if not os.path.isdir(outdir): os.mkdir(outdir) - + # get parameter values - lEmin,lEmax,alpha,gamma,sfr_n,logmean,logsigma,C=pset - Emin=10**lEmin - Emax=10**lEmax - + lEmin, lEmax, alpha, gamma, sfr_n, logmean, logsigma, C = pset + Emin = 10 ** lEmin + Emax = 10 ** lEmax + # generates a DM mask # creates a mask of values in DM space to convolve with the DM grid - mask=pcosmic.get_dm_mask(dmvals,(logmean,logsigma),zvals,plot=True) - efficiencies=survey.get_efficiency(dmvals) - + mask = pcosmic.get_dm_mask(dmvals, (logmean, logsigma), zvals, plot=True) + efficiencies = survey.get_efficiency(dmvals) + # get an initial grid with no beam values - grids=[] - bbs=[] - bos=[] - - - norms=np.zeros([len(binset)]) - numbins=np.zeros([len(binset)]) - - for k,nbins in enumerate(binset): - grid=zdm_grid.Grid() - grid.pass_grid(zDMgrid,zvals,dmvals) - grid.smear_dm(mask,logmean,logsigma) - grid.calc_thresholds(survey.meta['THRESH'],survey.mean_efficiencies,alpha=alpha) + grids = [] + bbs = [] + bos = [] + + norms = np.zeros([len(binset)]) + numbins = np.zeros([len(binset)]) + + for k, nbins in enumerate(binset): + grid = zdm_grid.Grid() + grid.pass_grid(zDMgrid, zvals, dmvals) + grid.smear_dm(mask, logmean, logsigma) + grid.calc_thresholds( + survey.meta["THRESH"], survey.mean_efficiencies, alpha=alpha + ) grid.calc_dV() - grid.set_evolution(sfr_n) # sets star-formation rate scaling with z - here, no evoltion... - - if nbins != 0 and nbins != 'all': - survey.init_beam(nbins=nbins,method=method,thresh=thresh) + grid.set_evolution( + sfr_n + ) # sets star-formation rate scaling with z - here, no evoltion... + + if nbins != 0 and nbins != "all": + survey.init_beam(nbins=nbins, method=method, thresh=thresh) bbs.append(np.copy(survey.beam_b)) bos.append(np.copy(survey.beam_o)) - grid.calc_pdv(Emin,Emax,gamma,survey.beam_b,survey.beam_o) # calculates volumetric-weighted probabilities - numbins[k]=nbins - elif nbins ==0: - grid.calc_pdv(Emin,Emax,gamma) # calculates volumetric-weighted probabilities + grid.calc_pdv( + Emin, Emax, gamma, survey.beam_b, survey.beam_o + ) # calculates volumetric-weighted probabilities + numbins[k] = nbins + elif nbins == 0: + grid.calc_pdv( + Emin, Emax, gamma + ) # calculates volumetric-weighted probabilities bbs.append(np.array([1])) bos.append(np.array([1])) - numbins[k]=nbins + numbins[k] = nbins else: - survey.init_beam(nbins=nbins,method=3,thresh=thresh) + survey.init_beam(nbins=nbins, method=3, thresh=thresh) bbs.append(np.copy(survey.beam_b)) bos.append(np.copy(survey.beam_o)) - numbins[k]=survey.beam_o.size - grid.calc_pdv(Emin,Emax,gamma,survey.beam_b,survey.beam_o) # calculates volumetric-weighted probabilities - - - grid.calc_rates() # calculates rates by multiplying above with pdm plot - name=outdir+'beam_test_'+survey.meta["BEAM"]+'_nbins_'+str(nbins)+'.pdf' - plot_grid_2(grid.rates,grid.zvals,grid.dmvals,zmax=zmax,DMmax=DMmax,name=name,norm=2,log=True,label='$f(DM_{\\rm EG},z)p(DM_{\\rm EG},z)dV$ [Mpc$^3$]',project=True) + numbins[k] = survey.beam_o.size + grid.calc_pdv( + Emin, Emax, gamma, survey.beam_b, survey.beam_o + ) # calculates volumetric-weighted probabilities + + grid.calc_rates() # calculates rates by multiplying above with pdm plot + name = ( + outdir + + "beam_test_" + + survey.meta["BEAM"] + + "_nbins_" + + str(nbins) + + ".pdf" + ) + plot_grid_2( + grid.rates, + grid.zvals, + grid.dmvals, + zmax=zmax, + DMmax=DMmax, + name=name, + norm=2, + log=True, + label="$f(DM_{\\rm EG},z)p(DM_{\\rm EG},z)dV$ [Mpc$^3$]", + project=True, + ) grids.append(grid) - + # OK, we now have a list of grids with various interpolating factors # we produce plots of the rate for each, and also difference plots with the best - #Does a linear plot relative to the best case - - - bestgrid=grids[-1] # we always have the worst grid at 0 - #bestgrid.rates=bestgrid.rates / np.sum(bestgrid.rates) - + # Does a linear plot relative to the best case + + bestgrid = grids[-1] # we always have the worst grid at 0 + # bestgrid.rates=bestgrid.rates / np.sum(bestgrid.rates) + # normalises - - for i,grid in enumerate(grids): - norms[i]=np.sum(grid.rates) - grid.rates=grid.rates / norms[i] - - np.save(outdir+survey.meta["BEAM"]+'_total_rates.npy',norms) - np.save(outdir+survey.meta["BEAM"]+'_nbins.npy',numbins) - - bestz=np.sum(grid.rates,axis=1) - bestdm=np.sum(grid.rates,axis=0) - + + for i, grid in enumerate(grids): + norms[i] = np.sum(grid.rates) + grid.rates = grid.rates / norms[i] + + np.save(outdir + survey.meta["BEAM"] + "_total_rates.npy", norms) + np.save(outdir + survey.meta["BEAM"] + "_nbins.npy", numbins) + + bestz = np.sum(grid.rates, axis=1) + bestdm = np.sum(grid.rates, axis=0) + ###### makes a 1d set of plots in dm and redshift ######## - font = {'family' : 'normal', - 'weight' : 'normal', - 'size' : 6} + font = {"family": "normal", "weight": "normal", "size": 6} - matplotlib.rc('font', **font) - fig,ax=plt.subplots(3,2,sharey='row',sharex='col')#,sharey='row',sharex='col') - - #ax[0,0]=fig.add_subplot(221) - #ax[0,1]=fig.add_subplot(222) - #ax[1,0]=fig.add_subplot(223) - #ax[1,1]=fig.add_subplot(224) - - #ax[0,0].plot(grid.zvals,bestz,color='black',label='All') - ax[1,0].set_xlabel('z') - ax[1,1].set_xlabel('DM_{\\rm EG}') - ax[2,0].set_xlabel('z') - ax[2,1].set_xlabel('DM_{\\rm EG}') - ax[0,0].set_ylabel('Abs') - ax[0,1].set_ylabel('Abs') - ax[1,0].set_ylabel('Dabs') - ax[1,1].set_ylabel('Dabs') - ax[2,0].set_ylabel('Rel diff') - ax[2,1].set_ylabel('Rel diff') - - # force relative range only - ax[2,0].set_ylim(-1,1) - ax[2,1].set_ylim(-1,1) - - ax[0,0].set_xlim(0,zmax) - ax[0,1].set_xlim(0,DMmax) - ax[1,0].set_xlim(0,zmax) - ax[2,0].set_xlim(0,zmax) - ax[1,1].set_xlim(0,DMmax) - ax[2,1].set_xlim(0,DMmax) - - ax[0,0].plot(grid.zvals,np.sum(bestgrid.rates,axis=1),label='All',color='black') - ax[0,1].plot(grid.dmvals,np.sum(bestgrid.rates,axis=0),label='All',color='black') - - for i,grid in enumerate(grids[:-1]): - - diff=grid.rates-bestgrid.rates - - #calculates relative and absolute errors in dm and z space - dz=np.sum(diff,axis=1) - ddm=np.sum(diff,axis=0) - rdz=dz/bestz - rdm=ddm/bestdm - - thisz=np.sum(grid.rates,axis=1) - thisdm=np.sum(grid.rates,axis=0) - - ax[0,0].plot(grid.zvals,thisz,label=str(binset[i])) - ax[0,1].plot(grid.dmvals,thisdm,label=str(binset[i])) - ax[1,0].plot(grid.zvals,dz,label=str(binset[i])) - ax[1,1].plot(grid.dmvals,ddm,label=str(binset[i])) - ax[2,0].plot(grid.zvals,rdz,label=str(binset[i])) - ax[2,1].plot(grid.dmvals,rdm,label=str(binset[i])) - ax[0,0].legend(fontsize=6) + matplotlib.rc("font", **font) + fig, ax = plt.subplots( + 3, 2, sharey="row", sharex="col" + ) # ,sharey='row',sharex='col') + + # ax[0,0]=fig.add_subplot(221) + # ax[0,1]=fig.add_subplot(222) + # ax[1,0]=fig.add_subplot(223) + # ax[1,1]=fig.add_subplot(224) + + # ax[0,0].plot(grid.zvals,bestz,color='black',label='All') + ax[1, 0].set_xlabel("z") + ax[1, 1].set_xlabel("DM_{\\rm EG}") + ax[2, 0].set_xlabel("z") + ax[2, 1].set_xlabel("DM_{\\rm EG}") + ax[0, 0].set_ylabel("Abs") + ax[0, 1].set_ylabel("Abs") + ax[1, 0].set_ylabel("Dabs") + ax[1, 1].set_ylabel("Dabs") + ax[2, 0].set_ylabel("Rel diff") + ax[2, 1].set_ylabel("Rel diff") + + # force relative range only + ax[2, 0].set_ylim(-1, 1) + ax[2, 1].set_ylim(-1, 1) + + ax[0, 0].set_xlim(0, zmax) + ax[0, 1].set_xlim(0, DMmax) + ax[1, 0].set_xlim(0, zmax) + ax[2, 0].set_xlim(0, zmax) + ax[1, 1].set_xlim(0, DMmax) + ax[2, 1].set_xlim(0, DMmax) + + ax[0, 0].plot( + grid.zvals, np.sum(bestgrid.rates, axis=1), label="All", color="black" + ) + ax[0, 1].plot( + grid.dmvals, np.sum(bestgrid.rates, axis=0), label="All", color="black" + ) + + for i, grid in enumerate(grids[:-1]): + + diff = grid.rates - bestgrid.rates + + # calculates relative and absolute errors in dm and z space + dz = np.sum(diff, axis=1) + ddm = np.sum(diff, axis=0) + rdz = dz / bestz + rdm = ddm / bestdm + + thisz = np.sum(grid.rates, axis=1) + thisdm = np.sum(grid.rates, axis=0) + + ax[0, 0].plot(grid.zvals, thisz, label=str(binset[i])) + ax[0, 1].plot(grid.dmvals, thisdm, label=str(binset[i])) + ax[1, 0].plot(grid.zvals, dz, label=str(binset[i])) + ax[1, 1].plot(grid.dmvals, ddm, label=str(binset[i])) + ax[2, 0].plot(grid.zvals, rdz, label=str(binset[i])) + ax[2, 1].plot(grid.dmvals, rdm, label=str(binset[i])) + ax[0, 0].legend(fontsize=6) plt.figure(fig.number) plt.tight_layout() - plt.savefig(outdir+'d_dm_z_'+survey.meta["BEAM"]+'_nbins_'+str(binset[i])+'.pdf') + plt.savefig( + outdir + "d_dm_z_" + survey.meta["BEAM"] + "_nbins_" + str(binset[i]) + ".pdf" + ) plt.close() - - acc=open(outdir+'accuracy.dat','w') - mean=np.mean(bestgrid.rates) - size=bestgrid.rates.size - string='#Nbins Acc StdDev StdDev/mean; mean={:.2E}\n'.format(mean) - acc.write('#Nbins Acc StdDev StdDev/mean; mean='+str(mean)+'\n') - - for i,grid in enumerate(grids[:-1]): - - diff=grid.rates-bestgrid.rates - - inaccuracy=np.sum(diff**2) - std_dev=(inaccuracy/size)**0.5 - rel_std_dev=std_dev/mean - #print("Beam with bins ",binset[i]," has total inaccuracy ",inaccuracy) - string="{:.0f} {:.2E} {:.2E} {:.2E}".format(binset[i],inaccuracy,std_dev,rel_std_dev) - acc.write(string+'\n') - name=outdir+'diff_beam_test_'+survey.meta["BEAM"]+'_nbins_'+str(binset[i])+'.pdf' - - plot_grid_2(diff,grid.zvals,grid.dmvals,zmax=zmax,DMmax=DMmax,name=name,norm=0,log=False,label='$f(DM_{\\rm EG},z)p(DM_{\\rm EG},z)dV$ [Mpc$^3$]',project=True) - diff=diff/bestgrid.rates - nans=np.isnan(diff) - diff[nans]=0. - name=outdir+'rel_diff_beam_test_'+survey.meta["BEAM"]+'_nbins_'+str(binset[i])+'.pdf' - plot_grid_2(diff,grid.zvals,grid.dmvals,zmax=zmax,DMmax=DMmax,name=name,norm=0,log=False,label='$f(DM_{\\rm EG},z)p(DM_{\\rm EG},z)dV$ [Mpc$^3$]',project=True) - - + + acc = open(outdir + "accuracy.dat", "w") + mean = np.mean(bestgrid.rates) + size = bestgrid.rates.size + string = "#Nbins Acc StdDev StdDev/mean; mean={:.2E}\n".format(mean) + acc.write("#Nbins Acc StdDev StdDev/mean; mean=" + str(mean) + "\n") + + for i, grid in enumerate(grids[:-1]): + + diff = grid.rates - bestgrid.rates + + inaccuracy = np.sum(diff ** 2) + std_dev = (inaccuracy / size) ** 0.5 + rel_std_dev = std_dev / mean + # print("Beam with bins ",binset[i]," has total inaccuracy ",inaccuracy) + string = "{:.0f} {:.2E} {:.2E} {:.2E}".format( + binset[i], inaccuracy, std_dev, rel_std_dev + ) + acc.write(string + "\n") + name = ( + outdir + + "diff_beam_test_" + + survey.meta["BEAM"] + + "_nbins_" + + str(binset[i]) + + ".pdf" + ) + + plot_grid_2( + diff, + grid.zvals, + grid.dmvals, + zmax=zmax, + DMmax=DMmax, + name=name, + norm=0, + log=False, + label="$f(DM_{\\rm EG},z)p(DM_{\\rm EG},z)dV$ [Mpc$^3$]", + project=True, + ) + diff = diff / bestgrid.rates + nans = np.isnan(diff) + diff[nans] = 0.0 + name = ( + outdir + + "rel_diff_beam_test_" + + survey.meta["BEAM"] + + "_nbins_" + + str(binset[i]) + + ".pdf" + ) + plot_grid_2( + diff, + grid.zvals, + grid.dmvals, + zmax=zmax, + DMmax=DMmax, + name=name, + norm=0, + log=False, + label="$f(DM_{\\rm EG},z)p(DM_{\\rm EG},z)dV$ [Mpc$^3$]", + project=True, + ) + acc.close() -def initialise_grids(surveys: list, zDMgrid: np.ndarray, - zvals: np.ndarray, - dmvals: np.ndarray, state:parameters.State, - wdist=True): + +def initialise_grids( + surveys: list, + zDMgrid: np.ndarray, + zvals: np.ndarray, + dmvals: np.ndarray, + state: parameters.State, + wdist=True, +): """ For a list of surveys, construct a zDMgrid object wdist indicates a distribution of widths in the survey, i.e. do not use the mean efficiency @@ -1553,17 +1930,17 @@ def initialise_grids(surveys: list, zDMgrid: np.ndarray, Returns: list: list of Grid objects """ - if not isinstance(surveys,list): - surveys=[surveys] - + if not isinstance(surveys, list): + surveys = [surveys] + # generates a DM mask # creates a mask of values in DM space to convolve with the DM grid - mask=pcosmic.get_dm_mask( - dmvals,(state.host.lmean,state.host.lsigma), - zvals,plot=True) - grids=[] + mask = pcosmic.get_dm_mask( + dmvals, (state.host.lmean, state.host.lsigma), zvals, plot=True + ) + grids = [] for survey in surveys: - ''' + """ if wdist: efficiencies=survey.efficiencies # two dimensions weights=survey.wplist @@ -1571,11 +1948,12 @@ def initialise_grids(surveys: list, zDMgrid: np.ndarray, efficiencies=survey.mean_efficiencies weights=None #efficiencies=survey.get_efficiency(dmvals) - ''' - - grid=zdm_grid.Grid(survey, copy.deepcopy(state), - zDMgrid, zvals, dmvals, mask, wdist) - ''' + """ + + grid = zdm_grid.Grid( + survey, copy.deepcopy(state), zDMgrid, zvals, dmvals, mask, wdist + ) + """ grid.pass_grid(zDMgrid,zvals,dmvals) grid.smear_dm(mask)#,logmean,logsigma) @@ -1590,102 +1968,191 @@ def initialise_grids(surveys: list, zDMgrid: np.ndarray, #survey.beam_o) # calculates volumetric-weighted probabilities grid.set_evolution() # sets star-formation rate scaling with z - here, no evoltion... grid.calc_rates() # calculates rates by multiplying above with pdm plot - ''' + """ grids.append(grid) - + return grids - + + def generate_example_plots(): """ Loads the lat50survey and generates some example plots """ - - #cos.set_cosmology(Omega_m=1.2) setup for cosmology + + # cos.set_cosmology(Omega_m=1.2) setup for cosmology cos.init_dist_measures() - - #parser.add_argument(", help + + # parser.add_argument(", help # get the grid of p(DM|z) - zDMgrid, zvals,dmvals=get_zdm_grid(new=False,plot=False,method='analytic') - pcosmic.plot_mean(zvals,'Plots/mean_DM.pdf') - - #load the lat50 survey data - lat50=survey.survey() - lat50.process_survey_file('Surveys/CRAFT_lat50.dat') - - efficiencies=lat50.get_efficiency(dmvals) + zDMgrid, zvals, dmvals = get_zdm_grid(new=False, plot=False, method="analytic") + pcosmic.plot_mean(zvals, "Plots/mean_DM.pdf") + + # load the lat50 survey data + lat50 = survey.survey() + lat50.process_survey_file("Surveys/CRAFT_lat50.dat") + + efficiencies = lat50.get_efficiency(dmvals) plot_efficiencies(lat50) - + # we now do the mean efficiency approximation - #mean_efficiencies=np.mean(efficiencies,axis=0) - #Fth=lat50.meta('THRESH') - + # mean_efficiencies=np.mean(efficiencies,axis=0) + # Fth=lat50.meta('THRESH') + # create a grid object - grid=zdm_grid.Grid() - grid.pass_grid(zDMgrid,zvals,dmvals) - + grid = zdm_grid.Grid() + grid.pass_grid(zDMgrid, zvals, dmvals) + # plots the grid of intrinsic p(DM|z) - plot_grid_2(grid.grid,grid.zvals,grid.dmvals,zmax=1,DMmax=1000,name='Plots/p_dm_z_grid_image.pdf',norm=1,log=True,label='$\\log_{10}p(DM_{\\rm EG}|z)$',conts=[0.16,0.5,0.88]) - + plot_grid_2( + grid.grid, + grid.zvals, + grid.dmvals, + zmax=1, + DMmax=1000, + name="Plots/p_dm_z_grid_image.pdf", + norm=1, + log=True, + label="$\\log_{10}p(DM_{\\rm EG}|z)$", + conts=[0.16, 0.5, 0.88], + ) + # creates a mask of values in DM space to convolve with the DM # grid # best-fit values-ish from green curves in fig 3 of cosmic dm paper - mean=125 - sigma=10**0.25 - logmean=np.log10(mean) - logsigma=np.log10(sigma) - mask=pcosmic.get_dm_mask(grid.dmvals,(logmean,logsigma),zvals,plot=True) - - grid.smear_dm(mask,logmean,logsigma) + mean = 125 + sigma = 10 ** 0.25 + logmean = np.log10(mean) + logsigma = np.log10(sigma) + mask = pcosmic.get_dm_mask(grid.dmvals, (logmean, logsigma), zvals, plot=True) + + grid.smear_dm(mask, logmean, logsigma) # plots the grid of intrinsic p(DM|z) - plot_grid_2(grid.smear_grid,grid.zvals,grid.dmvals,zmax=1,DMmax=1000,name='Plots/DMX_grid_image.pdf',norm=1,log=True,label='$\\log_{10}p(DM_{\\rm EG}|z)$') - #plot_grid_2(grid.smear_grid2,grid.zvals,grid.dmvals,zmax=1,DMmax=1000,name='DMX_grid_image2.pdf',norm=True,log=True,label='$\\log_{10}p(DM_{\\rm EG}|z)$') - + plot_grid_2( + grid.smear_grid, + grid.zvals, + grid.dmvals, + zmax=1, + DMmax=1000, + name="Plots/DMX_grid_image.pdf", + norm=1, + log=True, + label="$\\log_{10}p(DM_{\\rm EG}|z)$", + ) + # plot_grid_2(grid.smear_grid2,grid.zvals,grid.dmvals,zmax=1,DMmax=1000,name='DMX_grid_image2.pdf',norm=True,log=True,label='$\\log_{10}p(DM_{\\rm EG}|z)$') + # plots grid of effective thresholds - alpha=1.6 - grid.calc_thresholds(lat50.meta['THRESH'],lat50.mean_efficiencies,alpha=alpha) - plot_grid_2(grid.thresholds,grid.zvals,grid.dmvals,zmax=1,DMmax=1000,name='Plots/thresholds_dm_z_grid_image.pdf',norm=1,log=True,label='$\\log (E_{\\rm th})$ [erg]') - + alpha = 1.6 + grid.calc_thresholds(lat50.meta["THRESH"], lat50.mean_efficiencies, alpha=alpha) + plot_grid_2( + grid.thresholds, + grid.zvals, + grid.dmvals, + zmax=1, + DMmax=1000, + name="Plots/thresholds_dm_z_grid_image.pdf", + norm=1, + log=True, + label="$\\log (E_{\\rm th})$ [erg]", + ) + # calculates rates for given gamma etc - gamma=-0.7 - Emax=1e42 - Emin=1e30 + gamma = -0.7 + Emax = 1e42 + Emin = 1e30 grid.calc_dV() - - grid.calc_pdv(Emin,Emax,gamma) # calculates volumetric-weighted probabilities - grid.set_evolution(0) # sets star-formation rate scaling with z - here, no evoltion... - grid.calc_rates() # calculates rates by multiplying above with pdm plot - plot_grid_2(grid.pdv,grid.zvals,grid.dmvals,zmax=1,DMmax=1000,name='Plots/pdv.pdf',norm=True,log=True,label='$p(DM_{\\rm EG},z)dV$ [Mpc$^3$]') - plot_grid_2(grid.rates,grid.zvals,grid.dmvals,zmax=1,DMmax=1000,name='Plots/base_rate_dm_z_grid_image.pdf',norm=2,log=True,label='$f(DM_{\\rm EG},z)p(DM_{\\rm EG},z)dV$ [Mpc$^3$]') - plot_grid_2(grid.rates,grid.zvals,grid.dmvals,zmax=1,DMmax=1000,name='Plots/project_rate_dm_z_grid_image.pdf',norm=2,log=True,label='$f(DM_{\\rm EG},z)p(DM_{\\rm EG},z)dV$ [Mpc$^3$]',project=True) - - - it.calc_likelihoods_1D(grid.rates,grid.zvals,grid.dmvals,lat50.DMEGs) - plot_grid_2(grid.rates,grid.zvals,grid.dmvals,zmax=1,DMmax=1000,name='Plots/wFRB_project_rate.pdf',norm=2,log=True,label='$f(DM_{\\rm EG},z)p(DM_{\\rm EG},z)dV$ [Mpc$^3$]',project=True,FRBDM=lat50.DMEGs) - + + grid.calc_pdv(Emin, Emax, gamma) # calculates volumetric-weighted probabilities + grid.set_evolution( + 0 + ) # sets star-formation rate scaling with z - here, no evoltion... + grid.calc_rates() # calculates rates by multiplying above with pdm plot + plot_grid_2( + grid.pdv, + grid.zvals, + grid.dmvals, + zmax=1, + DMmax=1000, + name="Plots/pdv.pdf", + norm=True, + log=True, + label="$p(DM_{\\rm EG},z)dV$ [Mpc$^3$]", + ) + plot_grid_2( + grid.rates, + grid.zvals, + grid.dmvals, + zmax=1, + DMmax=1000, + name="Plots/base_rate_dm_z_grid_image.pdf", + norm=2, + log=True, + label="$f(DM_{\\rm EG},z)p(DM_{\\rm EG},z)dV$ [Mpc$^3$]", + ) + plot_grid_2( + grid.rates, + grid.zvals, + grid.dmvals, + zmax=1, + DMmax=1000, + name="Plots/project_rate_dm_z_grid_image.pdf", + norm=2, + log=True, + label="$f(DM_{\\rm EG},z)p(DM_{\\rm EG},z)dV$ [Mpc$^3$]", + project=True, + ) + + it.calc_likelihoods_1D(grid.rates, grid.zvals, grid.dmvals, lat50.DMEGs) + plot_grid_2( + grid.rates, + grid.zvals, + grid.dmvals, + zmax=1, + DMmax=1000, + name="Plots/wFRB_project_rate.pdf", + norm=2, + log=True, + label="$f(DM_{\\rm EG},z)p(DM_{\\rm EG},z)dV$ [Mpc$^3$]", + project=True, + FRBDM=lat50.DMEGs, + ) + ###### shows how to do a 1D scan of parameter values ####### - pset=it.set_defaults(grid) + pset = it.set_defaults(grid) it.print_pset(pset) # define set of values to scan over - lEmaxs=np.linspace(40,44,21) - likes=it.scan_likelihoods_1D(grid,pset,lat50,1,lEmaxs,norm=True) - plot_1d(lEmaxs,likes,'$E_{\\rm max}$','Plots/test_lik_fn_emax.pdf') - + lEmaxs = np.linspace(40, 44, 21) + likes = it.scan_likelihoods_1D(grid, pset, lat50, 1, lEmaxs, norm=True) + plot_1d(lEmaxs, likes, "$E_{\\rm max}$", "Plots/test_lik_fn_emax.pdf") + -def plot_1d(pvec,lset,xlabel,savename,showplot=False): +def plot_1d(pvec, lset, xlabel, savename, showplot=False): plt.figure() plt.xlabel(xlabel) - plt.ylabel('$\\ell($'+xlabel+'$)$') - plt.plot(pvec,lset) + plt.ylabel("$\\ell($" + xlabel + "$)$") + plt.plot(pvec, lset) plt.tight_layout() plt.savefig(savename) if showplot: plt.show() plt.close() - + + # generates grid based on Monte Carlo model -def get_zdm_grid(state:parameters.State, new=True, - plot=False,method='analytic', - nz=500,zmin=0.01,zmax=5,ndm=1400,dmmax=7000., - datdir='GridData',tag="", orig=False, - verbose=False, save=False, zlog=False): +def get_zdm_grid( + state: parameters.State, + new=True, + plot=False, + method="analytic", + nz=500, + zmin=0.01, + zmax=5, + ndm=1400, + dmmax=7000.0, + datdir="GridData", + tag="", + orig=False, + verbose=False, + save=False, + zlog=False, +): """Generate a grid of z vs. DM for an assumed F value for a specified z range and DM range. @@ -1720,249 +2187,333 @@ def get_zdm_grid(state:parameters.State, new=True, os.mkdir(datdir) except: pass - if method=='MC': - savefile=datdir+'/'+tag+'zdm_MC_grid_'+str(state.IGM.F)+'.npy' - datfile=datdir+'/'+tag+'zdm_MC_data_'+str(state.IGM.F)+'.npy' - zfile=datdir+'/'+tag+'zdm_MC_z_'+str(state.IGM.F)+'.npy' - dmfile=datdir+'/'+tag+'zdm_MC_dm_'+str(state.IGM.F)+'.npy' - elif method=='analytic': - savefile=datdir+'/'+tag+'zdm_A_grid_'+str(state.IGM.F)+'H0_'+str(state.cosmo.H0)+'.npy' - datfile=datdir+'/'+tag+'zdm_A_data_'+str(state.IGM.F)+'H0_'+str(state.cosmo.H0)+'.npy' - zfile=datdir+'/'+tag+'zdm_A_z_'+str(state.IGM.F)+'H0_'+str(state.cosmo.H0)+'.npy' - dmfile=datdir+'/'+tag+'zdm_A_dm_'+str(state.IGM.F)+'H0_'+str(state.cosmo.H0)+'.npy' - C0file=datdir+'/'+tag+'zdm_A_C0_'+str(state.IGM.F)+'H0_'+str(state.cosmo.H0)+'.npy' - #labelled pickled files with H0 + if method == "MC": + savefile = datdir + "/" + tag + "zdm_MC_grid_" + str(state.IGM.logF) + ".npy" + datfile = datdir + "/" + tag + "zdm_MC_data_" + str(state.IGM.logF) + ".npy" + zfile = datdir + "/" + tag + "zdm_MC_z_" + str(state.IGM.logF) + ".npy" + dmfile = datdir + "/" + tag + "zdm_MC_dm_" + str(state.IGM.logF) + ".npy" + elif method == "analytic": + savefile = ( + datdir + + "/" + + tag + + "zdm_A_grid_" + + str(state.IGM.logF) + + "H0_" + + str(state.cosmo.H0) + + ".npy" + ) + datfile = ( + datdir + + "/" + + tag + + "zdm_A_data_" + + str(state.IGM.logF) + + "H0_" + + str(state.cosmo.H0) + + ".npy" + ) + zfile = ( + datdir + + "/" + + tag + + "zdm_A_z_" + + str(state.IGM.logF) + + "H0_" + + str(state.cosmo.H0) + + ".npy" + ) + dmfile = ( + datdir + + "/" + + tag + + "zdm_A_dm_" + + str(state.IGM.logF) + + "H0_" + + str(state.cosmo.H0) + + ".npy" + ) + C0file = ( + datdir + + "/" + + tag + + "zdm_A_C0_" + + str(state.IGM.logF) + + "H0_" + + str(state.cosmo.H0) + + ".npy" + ) + # labelled pickled files with H0 if new: - - ddm=dmmax/ndm - + + ddm = dmmax / ndm + if zlog: # generates a pseudo-log spacing # grid values increase with \sqrt(log) - lzmax=np.log10(zmax) - lzmin=np.log10(zmin) - zvals=np.logspace(lzmin,lzmax,nz) + lzmax = np.log10(zmax) + lzmin = np.log10(zmin) + zvals = np.logspace(lzmin, lzmax, nz) else: - dz=zmax/nz - zvals=(np.arange(nz)+1)*dz - dmvals=(np.arange(ndm)+1)*ddm - - dmmeans=dmvals[1:] - (dmvals[1]-dmvals[0])/2. - zdmgrid=np.zeros([nz,ndm]) - - if method=='MC': + dz = zmax / nz + zvals = (np.arange(nz) + 1) * dz + dmvals = (np.arange(ndm) + 1) * ddm + + dmmeans = dmvals[1:] - (dmvals[1] - dmvals[0]) / 2.0 + zdmgrid = np.zeros([nz, ndm]) + + if method == "MC": # generate DM grid from the models if verbose: print("Generating the zdm Monte Carlo grid") - nfrb=10000 - t0=time.process_time() - DMs = dlas.monte_DM(np.array(zvals)*3000, nrand=nfrb) - #DMs *= 200000 #seems to be a good fit... - t1=time.process_time() - dt=t1-t0 - hists=[] - for i,z in enumerate(zvals): - hist,bins=np.histogram(DMs[:,i],bins=dmvals) + nfrb = 10000 + t0 = time.process_time() + DMs = dlas.monte_DM(np.array(zvals) * 3000, nrand=nfrb) + # DMs *= 200000 #seems to be a good fit... + t1 = time.process_time() + dt = t1 - t0 + hists = [] + for i, z in enumerate(zvals): + hist, bins = np.histogram(DMs[:, i], bins=dmvals) hists.append(hist) - all_hists=np.array(hists) - elif method=='analytic': + all_hists = np.array(hists) + elif method == "analytic": if verbose: print("Generating the zdm analytic grid") - t0=time.process_time() + t0 = time.process_time() # calculate constants for p_DM distribution if orig: - C0s=pcosmic.make_C0_grid(zvals,state.IGM.logF) + C0s = pcosmic.make_C0_grid(zvals, state.IGM.logF) else: f_C0_3 = cosmic.grab_C0_spline() - actual_F = 10**(state.IGM.logF) + actual_F = 10 ** (state.IGM.logF) sigma = actual_F / np.sqrt(zvals) C0s = f_C0_3(sigma) # generate pDM grid using those COs - zDMgrid=pcosmic.get_pDM_grid(state,dmvals,zvals,C0s,zlog=zlog) - - metadata=np.array([nz,ndm,state.IGM.logF]) + zDMgrid = pcosmic.get_pDM_grid(state, dmvals, zvals, C0s, zlog=zlog) + + metadata = np.array([nz, ndm, state.IGM.logF]) if save: - np.save(savefile,zDMgrid) - np.save(datfile,metadata) - np.save(zfile,zvals) - np.save(dmfile,dmvals) + np.save(savefile, zDMgrid) + np.save(datfile, metadata) + np.save(zfile, zvals) + np.save(dmfile, dmvals) else: - zDMgrid=np.load(savefile) - zvals=np.load(zfile) - dmvals=np.load(dmfile) - metadata=np.load(datfile) - nz,ndm,F=metadata - + zDMgrid = np.load(savefile) + zvals = np.load(zfile) + dmvals = np.load(dmfile) + metadata = np.load(datfile) + nz, ndm, F = metadata + if plot: plt.figure() - plt.xlabel('DM_{\\rm EG} [pc cm$^{-3}$]') - plt.ylabel('p(DM_{\\rm EG})') - - nplot=int(zvals.size/10) - for i,z in enumerate(zvals): - if i%nplot==0: - plt.plot(dmvals,zDMgrid[i,:],label='z='+str(z)[0:4]) + plt.xlabel("DM_{\\rm EG} [pc cm$^{-3}$]") + plt.ylabel("p(DM_{\\rm EG})") + + nplot = int(zvals.size / 10) + for i, z in enumerate(zvals): + if i % nplot == 0: + plt.plot(dmvals, zDMgrid[i, :], label="z=" + str(z)[0:4]) plt.legend() plt.tight_layout() - plt.savefig('p_dm_slices.pdf') + plt.savefig("p_dm_slices.pdf") plt.close() - - return zDMgrid, zvals,dmvals -def plot_zdm_basic_paper(zDMgrid,zvals,dmvals,zmax=1,DMmax=1000, - norm=0,log=True,name='temp.pdf',ylabel=None, - label='$\\log_{10}p(DM_{\\rm EG},z)$',project=False,conts=False,FRBZ=None, - FRBDM=None,title='Plot',H0=None,showplot=False): - ''' Plots basic distributions of z and dm for the paper ''' + return zDMgrid, zvals, dmvals + + +def plot_zdm_basic_paper( + zDMgrid, + zvals, + dmvals, + zmax=1, + DMmax=1000, + norm=0, + log=True, + name="temp.pdf", + ylabel=None, + label="$\\log_{10}p(DM_{\\rm EG},z)$", + project=False, + conts=False, + FRBZ=None, + FRBDM=None, + title="Plot", + H0=None, + showplot=False, +): + """ Plots basic distributions of z and dm for the paper """ if H0 is None: H0 = cos.cosmo.H0 - cmx = plt.get_cmap('cubehelix') - + cmx = plt.get_cmap("cubehelix") + ##### imshow of grid ####### - + # we protect these variables - zDMgrid=np.copy(zDMgrid) - zvals=np.copy(zvals) - dmvals=np.copy(dmvals) - + zDMgrid = np.copy(zDMgrid) + zvals = np.copy(zvals) + dmvals = np.copy(dmvals) + plt.figure() - #rect_2D=[0,0,1,1] - ax1=plt.axes() - + # rect_2D=[0,0,1,1] + ax1 = plt.axes() + plt.sca(ax1) - - plt.xlabel('z') + + plt.xlabel("z") if ylabel is not None: plt.ylabel(ylabel) else: - plt.ylabel('${\\rm DM}_{\\rm EG}$') - - nz,ndm=zDMgrid.shape - - - ixmax=np.where(zvals > zmax)[0] - if len(ixmax) >0: - zvals=zvals[:ixmax[0]] - nz=zvals.size - zDMgrid=zDMgrid[:ixmax[0],:] - + plt.ylabel("${\\rm DM}_{\\rm EG}$") + + nz, ndm = zDMgrid.shape + + ixmax = np.where(zvals > zmax)[0] + if len(ixmax) > 0: + zvals = zvals[: ixmax[0]] + nz = zvals.size + zDMgrid = zDMgrid[: ixmax[0], :] + ### generates contours *before* cutting array in DM ### ### might need to normalise contours by integer lengths, oh well! ### if conts: nc = len(conts) - carray=np.zeros([nc,nz]) + carray = np.zeros([nc, nz]) for i in np.arange(nz): - cdf=np.cumsum(zDMgrid[i,:]) + cdf = np.cumsum(zDMgrid[i, :]) cdf /= cdf[-1] - - for j,c in enumerate(conts): - less=np.where(cdf < c)[0] - - if len(less)==0: - carray[j,i]=0. - dmc=0. - il1=0 - il2=0 + + for j, c in enumerate(conts): + less = np.where(cdf < c)[0] + + if len(less) == 0: + carray[j, i] = 0.0 + dmc = 0.0 + il1 = 0 + il2 = 0 else: - il1=less[-1] - - if il1 == ndm-1: - il1=ndm-2 - - il2=il1+1 - k1=(cdf[il2]-c)/(cdf[il2]-cdf[il1]) - dmc=k1*dmvals[il1]+(1.-k1)*dmvals[il2] - carray[j,i]=dmc - - ddm=dmvals[1]-dmvals[0] - carray /= ddm # turns this into integer units for plotting - - iymax=np.where(dmvals > DMmax)[0] - if len(iymax)>0: - dmvals=dmvals[:iymax[0]] - zDMgrid=zDMgrid[:,:iymax[0]] - ndm=dmvals.size - + il1 = less[-1] + + if il1 == ndm - 1: + il1 = ndm - 2 + + il2 = il1 + 1 + k1 = (cdf[il2] - c) / (cdf[il2] - cdf[il1]) + dmc = k1 * dmvals[il1] + (1.0 - k1) * dmvals[il2] + carray[j, i] = dmc + + ddm = dmvals[1] - dmvals[0] + carray /= ddm # turns this into integer units for plotting + + iymax = np.where(dmvals > DMmax)[0] + if len(iymax) > 0: + dmvals = dmvals[: iymax[0]] + zDMgrid = zDMgrid[:, : iymax[0]] + ndm = dmvals.size + # currently this is "per cell" - now to change to "per DM" # normalises the grid by the bin width, i.e. probability per bin, not probability density - ddm=dmvals[1]-dmvals[0] - dz=zvals[1]-zvals[0] - - zDMgrid /= ddm # norm=1 - + ddm = dmvals[1] - dmvals[0] + dz = zvals[1] - zvals[0] + + zDMgrid /= ddm # norm=1 + # checks against zeros for a log-plot - orig=np.copy(zDMgrid) - zDMgrid=zDMgrid.reshape(zDMgrid.size) - setzero=np.where(zDMgrid==0.) - zDMgrid=np.log10(zDMgrid) - zDMgrid[setzero]=-100 - zDMgrid=zDMgrid.reshape(nz,ndm) - + orig = np.copy(zDMgrid) + zDMgrid = zDMgrid.reshape(zDMgrid.size) + setzero = np.where(zDMgrid == 0.0) + zDMgrid = np.log10(zDMgrid) + zDMgrid[setzero] = -100 + zDMgrid = zDMgrid.reshape(nz, ndm) + # gets a square plot - aspect=nz/float(ndm) - - # sets the x and y tics - xtvals=np.arange(zvals.size) - everx=int(zvals.size/5) - plt.xticks(xtvals[everx-1::everx],zvals[everx-1::everx]) - - ytvals=np.arange(dmvals.size) - every=int(dmvals.size/5) - plt.yticks(ytvals[every-1::every],dmvals[every-1::every]) - - im=plt.imshow(zDMgrid.T,cmap=cmx,origin='lower', interpolation='None',aspect=aspect) - + aspect = nz / float(ndm) + + # sets the x and y tics + xtvals = np.arange(zvals.size) + everx = int(zvals.size / 5) + plt.xticks(xtvals[everx - 1 :: everx], zvals[everx - 1 :: everx]) + + ytvals = np.arange(dmvals.size) + every = int(dmvals.size / 5) + plt.yticks(ytvals[every - 1 :: every], dmvals[every - 1 :: every]) + + im = plt.imshow( + zDMgrid.T, cmap=cmx, origin="lower", interpolation="None", aspect=aspect + ) + ###### gets decent axis labels, down to 1 decimal place ####### - ax=plt.gca() + ax = plt.gca() labels = [item.get_text() for item in ax.get_xticklabels()] for i in np.arange(len(labels)): - labels[i]=labels[i][0:4] + labels[i] = labels[i][0:4] ax.set_xticklabels(labels) labels = [item.get_text() for item in ax.get_yticklabels()] for i in np.arange(len(labels)): - if '.' in labels[i]: - labels[i]=labels[i].split('.')[0] + if "." in labels[i]: + labels[i] = labels[i].split(".")[0] ax.set_yticklabels(labels) ax.yaxis.labelpad = 0 - + # plots contours i there - cls=[":","--","-","--",":"] + cls = [":", "--", "-", "--", ":"] if conts: - plt.ylim(0,ndm-1) + plt.ylim(0, ndm - 1) for i in np.arange(nc): - j=int(nc-i-1) - plt.plot(np.arange(nz),carray[j,:],label=str(conts[j]),color='white',linestyle=cls[i]) - l=plt.legend(loc='lower right',fontsize=12) - #l=plt.legend(bbox_to_anchor=(0.2, 0.8),fontsize=8) + j = int(nc - i - 1) + plt.plot( + np.arange(nz), + carray[j, :], + label=str(conts[j]), + color="white", + linestyle=cls[i], + ) + l = plt.legend(loc="lower right", fontsize=12) + # l=plt.legend(bbox_to_anchor=(0.2, 0.8),fontsize=8) for text in l.get_texts(): - text.set_color("white") - - + text.set_color("white") + # limit to a reasonable range if logscale - themax=zDMgrid.max() - themin=int(themax-4) - themax=int(themax) - plt.clim(themin,themax) - - cbar=plt.colorbar(im,fraction=0.046, shrink=1.2,aspect=15,pad=0.05) + themax = zDMgrid.max() + themin = int(themax - 4) + themax = int(themax) + plt.clim(themin, themax) + + cbar = plt.colorbar(im, fraction=0.046, shrink=1.2, aspect=15, pad=0.05) cbar.set_label(label) - plt.clim(-4,0) + plt.clim(-4, 0) plt.tight_layout() - + plt.savefig(name) - plt.title(title+str(H0)) + plt.title(title + str(H0)) if showplot: plt.show() - plt.close() - -def plot_grid_2(zDMgrid,zvals,dmvals, - zmax=1,DMmax=1000,norm=0,log=True,name='temp.pdf', - label='$\\log_{10}p(DM_{\\rm EG},z)$',ylabel='${\\rm DM}_{\\rm EG}$', - project=False,conts=False, - FRBZ=None,FRBDM=None,Aconts=False, - Macquart=None,title="Plot", cmap=None, - H0=None,showplot=False,DMlines=None, - data_clr='red'): + plt.close() + + +def plot_grid_2( + zDMgrid, + zvals, + dmvals, + zmax=1, + DMmax=1000, + norm=0, + log=True, + name="temp.pdf", + label="$\\log_{10}p(DM_{\\rm EG},z)$", + ylabel="${\\rm DM}_{\\rm EG}$", + project=False, + conts=False, + FRBZ=None, + FRBDM=None, + Aconts=False, + Macquart=None, + title="Plot", + cmap=None, + H0=None, + showplot=False, + DMlines=None, + data_clr="red", +): """ Very complicated routine for plotting 2D zdm grids @@ -1993,83 +2544,82 @@ def plot_grid_2(zDMgrid,zvals,dmvals, if H0 is None: H0 = cos.cosmo.H0 if cmap is None: - cmx = plt.get_cmap('cubehelix') + cmx = plt.get_cmap("cubehelix") else: cmx = plt.get_cmap(cmap) - + ##### imshow of grid ####### - + # we protect these variables - zDMgrid=np.copy(zDMgrid) - zvals=np.copy(zvals) - dmvals=np.copy(dmvals) - - if (project): + zDMgrid = np.copy(zDMgrid) + zvals = np.copy(zvals) + dmvals = np.copy(dmvals) + + if project: plt.figure(1, figsize=(8, 8)) left, width = 0.1, 0.65 bottom, height = 0.1, 0.65 - gap=0.02 - woff=width+gap+left - hoff=height+gap+bottom - dw=1.-woff-gap - dh=1.-hoff-gap - - delta=1-height-bottom-0.05 - gap=0.11 + gap = 0.02 + woff = width + gap + left + hoff = height + gap + bottom + dw = 1.0 - woff - gap + dh = 1.0 - hoff - gap + + delta = 1 - height - bottom - 0.05 + gap = 0.11 rect_2D = [left, bottom, width, height] rect_1Dx = [left, hoff, width, dh] rect_1Dy = [woff, bottom, dw, height] - rect_cb = [woff,hoff,dw*0.5,dh] - ax1=plt.axes(rect_2D) - axx=plt.axes(rect_1Dx) - axy=plt.axes(rect_1Dy) - acb=plt.axes(rect_cb) - #axx.xaxis.set_major_formatter(NullFormatter()) - #axy.yaxis.set_major_formatter(NullFormatter()) + rect_cb = [woff, hoff, dw * 0.5, dh] + ax1 = plt.axes(rect_2D) + axx = plt.axes(rect_1Dx) + axy = plt.axes(rect_1Dy) + acb = plt.axes(rect_cb) + # axx.xaxis.set_major_formatter(NullFormatter()) + # axy.yaxis.set_major_formatter(NullFormatter()) else: plt.figure() - #rect_2D=[0,0,1,1] - ax1=plt.axes() - + # rect_2D=[0,0,1,1] + ax1 = plt.axes() + plt.sca(ax1) - - plt.xlabel('z') + + plt.xlabel("z") plt.ylabel(ylabel) - #plt.title(title+str(H0)) # I have removed this default title, use a file naming convention instead - - nz,ndm=zDMgrid.shape - - - ixmax=np.where(zvals > zmax)[0] - if len(ixmax) >0: - zvals=zvals[:ixmax[0]] - nz=zvals.size - zDMgrid=zDMgrid[:ixmax[0],:] - + # plt.title(title+str(H0)) # I have removed this default title, use a file naming convention instead + + nz, ndm = zDMgrid.shape + + ixmax = np.where(zvals > zmax)[0] + if len(ixmax) > 0: + zvals = zvals[: ixmax[0]] + nz = zvals.size + zDMgrid = zDMgrid[: ixmax[0], :] + # currently this is "per cell" - now to change to "per DM" # normalises the grid by the bin width, i.e. probability per bin, not probability density - ddm=dmvals[1]-dmvals[0] - dz=zvals[1]-zvals[0] - if norm==1: + ddm = dmvals[1] - dmvals[0] + dz = zvals[1] - zvals[0] + if norm == 1: zDMgrid /= ddm - #if Aconts: + # if Aconts: # alevels /= ddm - elif norm==2: - xnorm=np.sum(zDMgrid) + elif norm == 2: + xnorm = np.sum(zDMgrid) zDMgrid /= xnorm - #if Aconts: + # if Aconts: # alevels /= xnorm - elif norm==3: + elif norm == 3: zDMgrid /= np.max(zDMgrid) - + # sets contours according to norm if Aconts: - slist=np.sort(zDMgrid.flatten()) - cslist=np.cumsum(slist) + slist = np.sort(zDMgrid.flatten()) + cslist = np.cumsum(slist) cslist /= cslist[-1] - nAc=len(Aconts) - alevels=np.zeros([nAc]) - for i,ac in enumerate(Aconts): + nAc = len(Aconts) + alevels = np.zeros([nAc]) + for i, ac in enumerate(Aconts): # cslist is the cumulative probability distribution # Where cslist > ac determines the integer locations # of all cells exceeding the threshold @@ -2077,311 +2627,337 @@ def plot_grid_2(zDMgrid,zvals,dmvals, # the threshold # The value of slist at that point is the # level of the countour to draw - iwhich=np.where(cslist > ac)[0][0] - alevels[i]=slist[iwhich] - + iwhich = np.where(cslist > ac)[0][0] + alevels[i] = slist[iwhich] + if norm == 1: alevels /= ddm elif norm == 2: alevels /= xnorm - + ### generates contours *before* cutting array in DM ### ### might need to normalise contours by integer lengths, oh well! ### if conts: nc = len(conts) - carray=np.zeros([nc,nz]) + carray = np.zeros([nc, nz]) for i in np.arange(nz): - cdf=np.cumsum(zDMgrid[i,:]) + cdf = np.cumsum(zDMgrid[i, :]) cdf /= cdf[-1] - - for j,c in enumerate(conts): - less=np.where(cdf < c)[0] - - if len(less)==0: - carray[j,i]=0. - dmc=0. - il1=0 - il2=0 + + for j, c in enumerate(conts): + less = np.where(cdf < c)[0] + + if len(less) == 0: + carray[j, i] = 0.0 + dmc = 0.0 + il1 = 0 + il2 = 0 else: - il1=less[-1] - - if il1 == ndm-1: - il1=ndm-2 - - il2=il1+1 - k1=(cdf[il2]-c)/(cdf[il2]-cdf[il1]) - dmc=k1*dmvals[il1]+(1.-k1)*dmvals[il2] - carray[j,i]=dmc - - ddm=dmvals[1]-dmvals[0] - carray /= ddm # turns this into integer units for plotting - - iymax=np.where(dmvals > DMmax)[0] - if len(iymax)>0: - dmvals=dmvals[:iymax[0]] - zDMgrid=zDMgrid[:,:iymax[0]] - ndm=dmvals.size - + il1 = less[-1] + + if il1 == ndm - 1: + il1 = ndm - 2 + + il2 = il1 + 1 + k1 = (cdf[il2] - c) / (cdf[il2] - cdf[il1]) + dmc = k1 * dmvals[il1] + (1.0 - k1) * dmvals[il2] + carray[j, i] = dmc + + ddm = dmvals[1] - dmvals[0] + carray /= ddm # turns this into integer units for plotting + + iymax = np.where(dmvals > DMmax)[0] + if len(iymax) > 0: + dmvals = dmvals[: iymax[0]] + zDMgrid = zDMgrid[:, : iymax[0]] + ndm = dmvals.size + if log: # checks against zeros for a log-plot - orig=np.copy(zDMgrid) - zDMgrid=zDMgrid.reshape(zDMgrid.size) - setzero=np.where(zDMgrid==0.) - zDMgrid=np.log10(zDMgrid) - zDMgrid[setzero]=-100 - zDMgrid=zDMgrid.reshape(nz,ndm) + orig = np.copy(zDMgrid) + zDMgrid = zDMgrid.reshape(zDMgrid.size) + setzero = np.where(zDMgrid == 0.0) + zDMgrid = np.log10(zDMgrid) + zDMgrid[setzero] = -100 + zDMgrid = zDMgrid.reshape(nz, ndm) if Aconts: - alevels=np.log10(alevels) + alevels = np.log10(alevels) else: - orig=zDMgrid - + orig = zDMgrid + # gets a square plot - aspect=nz/float(ndm) - - # sets the x and y tics - xtvals=np.arange(zvals.size) - everx=int(zvals.size/5) - plt.xticks(xtvals[everx-1::everx],zvals[everx-1::everx]) - - ytvals=np.arange(dmvals.size) - every=int(dmvals.size/5) - plt.yticks(ytvals[every-1::every],dmvals[every-1::every]) - - im=plt.imshow(zDMgrid.T,cmap=cmx,origin='lower', interpolation='None',aspect=aspect) - + aspect = nz / float(ndm) + + # sets the x and y tics + xtvals = np.arange(zvals.size) + everx = int(zvals.size / 5) + plt.xticks(xtvals[everx - 1 :: everx], zvals[everx - 1 :: everx]) + + ytvals = np.arange(dmvals.size) + every = int(dmvals.size / 5) + plt.yticks(ytvals[every - 1 :: every], dmvals[every - 1 :: every]) + + im = plt.imshow( + zDMgrid.T, cmap=cmx, origin="lower", interpolation="None", aspect=aspect + ) + if Aconts: - styles=['--','-.',':'] - ax=plt.gca() - cs=ax.contour(zDMgrid.T,levels=alevels,origin='lower',colors="white",linestyles=styles) - #plt.clim(0,2e-5) - #ax.clabel(cs, cs.levels, inline=True, fontsize=10,fmt=['0.5','0.1','0.01']) + styles = ["--", "-.", ":"] + ax = plt.gca() + cs = ax.contour( + zDMgrid.T, levels=alevels, origin="lower", colors="white", linestyles=styles + ) + # plt.clim(0,2e-5) + # ax.clabel(cs, cs.levels, inline=True, fontsize=10,fmt=['0.5','0.1','0.01']) ###### gets decent axis labels, down to 1 decimal place ####### - ax=plt.gca() + ax = plt.gca() labels = [item.get_text() for item in ax.get_xticklabels()] for i in np.arange(len(labels)): - labels[i]=labels[i][0:4] + labels[i] = labels[i][0:4] ax.set_xticklabels(labels) labels = [item.get_text() for item in ax.get_yticklabels()] for i in np.arange(len(labels)): - if '.' in labels[i]: - labels[i]=labels[i].split('.')[0] + if "." in labels[i]: + labels[i] = labels[i].split(".")[0] ax.set_yticklabels(labels) ax.yaxis.labelpad = 0 - + # draw horizontal lines for a fixed DM if DMlines is not None: if log: - tempgrid=np.copy(zDMgrid) + tempgrid = np.copy(zDMgrid) tempgrid = zDMgrid - np.max(zDMgrid) - tempgrid = 10.**zDMgrid + tempgrid = 10.0 ** zDMgrid else: - tempgrid=zDMgrid + tempgrid = zDMgrid for DM in DMlines: - if DM>np.max(dmvals): - print("Cannot draw DM line ",DM," - range ",np.max(dmvals)," too small...") + if DM > np.max(dmvals): + print( + "Cannot draw DM line ", + DM, + " - range ", + np.max(dmvals), + " too small...", + ) continue # determines how far to draw line - iDM2=np.where(dmvals > DM)[0][0] # lowest value - iDM1=iDM2-1 - kDM=(DM-dmvals[iDM1])/(dmvals[iDM2]-dmvals[iDM1]) - cDM1=np.cumsum(tempgrid[:,iDM1]) + iDM2 = np.where(dmvals > DM)[0][0] # lowest value + iDM1 = iDM2 - 1 + kDM = (DM - dmvals[iDM1]) / (dmvals[iDM2] - dmvals[iDM1]) + cDM1 = np.cumsum(tempgrid[:, iDM1]) cDM1 /= cDM1[-1] - cDM2=np.cumsum(tempgrid[:,iDM2]) + cDM2 = np.cumsum(tempgrid[:, iDM2]) cDM2 /= cDM2[-1] - stop1=np.where(cDM1 < 0.99)[0][-1] - stop2=np.where(cDM2 < 0.99)[0][-1] - zstop = kDM*zvals[stop2] + (1.-kDM)*zvals[stop1] - zstop /= (zvals[1]-zvals[0]) - DM /= (dmvals[1]-dmvals[0]) - plt.plot([0,zstop],[DM,DM],color=data_clr, linestyle=':') - + stop1 = np.where(cDM1 < 0.99)[0][-1] + stop2 = np.where(cDM2 < 0.99)[0][-1] + zstop = kDM * zvals[stop2] + (1.0 - kDM) * zvals[stop1] + zstop /= zvals[1] - zvals[0] + DM /= dmvals[1] - dmvals[0] + plt.plot([0, zstop], [DM, DM], color=data_clr, linestyle=":") + # plots contours i there if conts: - plt.ylim(0,ndm-1) + plt.ylim(0, ndm - 1) for i in np.arange(nc): - j=int(nc-i-1) - plt.plot(np.arange(nz),carray[j,:],label=str(conts[j]),color='white') - l=plt.legend(loc='upper left',fontsize=8) - #l=plt.legend(bbox_to_anchor=(0.2, 0.8),fontsize=8) + j = int(nc - i - 1) + plt.plot(np.arange(nz), carray[j, :], label=str(conts[j]), color="white") + l = plt.legend(loc="upper left", fontsize=8) + # l=plt.legend(bbox_to_anchor=(0.2, 0.8),fontsize=8) for text in l.get_texts(): - text.set_color("white") + text.set_color("white") if Macquart is not None: # Note this is the Median for the lognormal, not the mean - muDMhost=np.log(10**Macquart.host.lmean) - sigmaDMhost=np.log(10**Macquart.host.lsigma) - meanHost = np.exp(muDMhost + sigmaDMhost**2/2.) - medianHost = np.exp(muDMhost) - #print(f"Host: mean={meanHost}, median={medianHost}") - plt.ylim(0,ndm-1) - plt.xlim(0,nz-1) - zmax=zvals[-1] - nz=zvals.size - #DMbar, zeval = igm.average_DM(zmax, cumul=True, neval=nz+1) + muDMhost = np.log(10 ** Macquart.host.lmean) + sigmaDMhost = np.log(10 ** Macquart.host.lsigma) + meanHost = np.exp(muDMhost + sigmaDMhost ** 2 / 2.0) + medianHost = np.exp(muDMhost) + # print(f"Host: mean={meanHost}, median={medianHost}") + plt.ylim(0, ndm - 1) + plt.xlim(0, nz - 1) + zmax = zvals[-1] + nz = zvals.size + # DMbar, zeval = igm.average_DM(zmax, cumul=True, neval=nz+1) DM_cosmic = pcosmic.get_mean_DM(zvals, Macquart) - - #idea is that 1 point is 1, hence... - zeval = zvals/dz - DMEG_mean = (DM_cosmic+meanHost)/ddm - DMEG_median = (DM_cosmic+medianHost)/ddm - plt.plot(zeval,DMEG_mean,color='blue',linewidth=2, - label='Macquart relation (mean)') + + # idea is that 1 point is 1, hence... + zeval = zvals / dz + DMEG_mean = (DM_cosmic + meanHost) / ddm + DMEG_median = (DM_cosmic + medianHost) / ddm + plt.plot( + zeval, + DMEG_mean, + color="blue", + linewidth=2, + label="Macquart relation (mean)", + ) # removed median, because it is only media of HOST not DM cosmic - #plt.plot(zeval,DMEG_median,color='blue', + # plt.plot(zeval,DMEG_median,color='blue', # linewidth=2, ls='--', # label='Macquart relation (median)') - l=plt.legend(loc='lower right',fontsize=12) - #l=plt.legend(bbox_to_anchor=(0.2, 0.8),fontsize=8) - #for text in l.get_texts(): - # text.set_color("white") - + l = plt.legend(loc="lower right", fontsize=12) + # l=plt.legend(bbox_to_anchor=(0.2, 0.8),fontsize=8) + # for text in l.get_texts(): + # text.set_color("white") + # limit to a reasonable range if logscale if log: - themax=np.nanmax(zDMgrid) - themin=int(themax-4) - themax=int(themax) - plt.clim(themin,themax) - + themax = np.nanmax(zDMgrid) + themin = int(themax - 4) + themax = int(themax) + plt.clim(themin, themax) + ##### add FRB host galaxies at some DM/redshift ##### if FRBZ is not None: - iDMs=FRBDM/ddm - iZ=FRBZ/dz - OK = np.where(FRBZ>0)[0] - plt.plot(iZ[OK],iDMs[OK],'o', color=data_clr, linestyle="") - + iDMs = FRBDM / ddm + iZ = FRBZ / dz + OK = np.where(FRBZ > 0)[0] + plt.plot(iZ[OK], iDMs[OK], "o", color=data_clr, linestyle="") + # do 1-D projected plots if project: plt.sca(acb) - cbar=plt.colorbar(im,fraction=0.046, shrink=1.2,aspect=20,pad=0.00,cax = acb) + cbar = plt.colorbar( + im, fraction=0.046, shrink=1.2, aspect=20, pad=0.00, cax=acb + ) cbar.ax.tick_params(labelsize=6) - cbar.set_label(label,fontsize=8) - + cbar.set_label(label, fontsize=8) + axy.set_yticklabels([]) - #axy.set_xticklabels([]) - #axx.set_yticklabels([]) + # axy.set_xticklabels([]) + # axx.set_yticklabels([]) axx.set_xticklabels([]) - yonly=np.sum(orig,axis=0) - xonly=np.sum(orig,axis=1) - - axy.plot(yonly,dmvals) # DM is the vertical axis now - axx.plot(zvals,xonly) - + yonly = np.sum(orig, axis=0) + xonly = np.sum(orig, axis=1) + + axy.plot(yonly, dmvals) # DM is the vertical axis now + axx.plot(zvals, xonly) + # if plotting DM only, put this on the axy axis showing DM distribution if FRBDM is not None: - hvals=np.zeros(FRBDM.size) - for i,DM in enumerate(FRBDM): - hvals[i]=yonly[np.where(dmvals > DM)[0][0]] - - axy.plot(hvals,FRBDM,'ro',linestyle="") + hvals = np.zeros(FRBDM.size) + for i, DM in enumerate(FRBDM): + hvals[i] = yonly[np.where(dmvals > DM)[0][0]] + + axy.plot(hvals, FRBDM, "ro", linestyle="") for tick in axy.yaxis.get_major_ticks(): - tick.label.set_fontsize(6) - + tick.label.set_fontsize(6) + if FRBZ is not None: - OK = np.where(FRBZ>0)[0] - hvals=np.zeros(FRBZ[OK].size) - for i,Z in enumerate(FRBZ[OK]): - hvals[i]=xonly[np.where(zvals > Z)[0][0]] - axx.plot(FRBZ[OK],hvals,'o', color=data_clr, linestyle="") + OK = np.where(FRBZ > 0)[0] + hvals = np.zeros(FRBZ[OK].size) + for i, Z in enumerate(FRBZ[OK]): + hvals[i] = xonly[np.where(zvals > Z)[0][0]] + axx.plot(FRBZ[OK], hvals, "o", color=data_clr, linestyle="") for tick in axx.xaxis.get_major_ticks(): - tick.label.set_fontsize(6) + tick.label.set_fontsize(6) else: - cbar=plt.colorbar(im,fraction=0.046, shrink=1.2,aspect=15,pad=0.05) + cbar = plt.colorbar(im, fraction=0.046, shrink=1.2, aspect=15, pad=0.05) cbar.set_label(label) plt.tight_layout() - + plt.savefig(name, dpi=300) if showplot: plt.show() plt.close() -def plot_grid(grid,zvals,dmvals,showplot=False): - ''' Plots a simple 2D grid ''' - cmx = plt.get_cmap('cubehelix') - + +def plot_grid(grid, zvals, dmvals, showplot=False): + """ Plots a simple 2D grid """ + cmx = plt.get_cmap("cubehelix") + plt.figure() - plt.zlabel('z') - plt.ylabel('DM_{\\rm EG}') - - plt.xtics(np.arange(zvals.size)[::10],zvals[::10]) - plt.ytics(np.arange(dmvals.size)[::10],dmvals[::10]) - plt.imshow(grid,extent=(zvals[0],zvals[-1],dmvals[0],dmvals[-1]),origin='lower',cmap=cmx) - cbar=plt.colorbar() - cbar.set_label('$p(DM_{\\rm EG}|z)') + plt.zlabel("z") + plt.ylabel("DM_{\\rm EG}") + + plt.xtics(np.arange(zvals.size)[::10], zvals[::10]) + plt.ytics(np.arange(dmvals.size)[::10], dmvals[::10]) + plt.imshow( + grid, + extent=(zvals[0], zvals[-1], dmvals[0], dmvals[-1]), + origin="lower", + cmap=cmx, + ) + cbar = plt.colorbar() + cbar.set_label("$p(DM_{\\rm EG}|z)") if showplot: plt.show() -def plot_efficiencies_paper(survey,savename,label): - ''' Plots a final version of efficiencies for the purpose of the paper ''' - dm=survey.DMlist - eff1s=survey.get_efficiency(dm,model="Quadrature",dsmear=True) - eff1mean=np.copy(survey.mean_efficiencies) - eff2s=survey.get_efficiency(dm,model="Sammons",dsmear=True) - eff2mean=np.copy(survey.mean_efficiencies) - DMobs=survey.DMs - NDM=DMobs.size - DMx=np.zeros([NDM]) - DMy=np.zeros([NDM]) - for i,DMo in enumerate(DMobs): - pos=np.where(dm > DMo)[0][0] - DMy[i]=eff1s[i,pos] - DMx[i]=dm[pos] - - + +def plot_efficiencies_paper(survey, savename, label): + """ Plots a final version of efficiencies for the purpose of the paper """ + dm = survey.DMlist + eff1s = survey.get_efficiency(dm, model="Quadrature", dsmear=True) + eff1mean = np.copy(survey.mean_efficiencies) + eff2s = survey.get_efficiency(dm, model="Sammons", dsmear=True) + eff2mean = np.copy(survey.mean_efficiencies) + DMobs = survey.DMs + NDM = DMobs.size + DMx = np.zeros([NDM]) + DMy = np.zeros([NDM]) + for i, DMo in enumerate(DMobs): + pos = np.where(dm > DMo)[0][0] + DMy[i] = eff1s[i, pos] + DMx[i] = dm[pos] + plt.figure() - plt.ylim(0,1) - - plt.text(1500,0.9,label) - - eff=survey.efficiencies + plt.ylim(0, 1) + + plt.text(1500, 0.9, label) + + eff = survey.efficiencies if "ID" in survey.frbs: - labels=survey.frbs["ID"] + labels = survey.frbs["ID"] else: - labels=np.arange(survey.NFRB) - - plt.xlabel('DM [pc cm$^{-3}$]') - plt.ylabel('Efficiency $\\epsilon$')#\\dfrac{F_{\\rm 1\,ms}}{F_{\\rm th}}$') - - ls=['-',':','--','-.'] - + labels = np.arange(survey.NFRB) + + plt.xlabel("DM [pc cm$^{-3}$]") + plt.ylabel("Efficiency $\\epsilon$") # \\dfrac{F_{\\rm 1\,ms}}{F_{\\rm th}}$') + + ls = ["-", ":", "--", "-."] + for i in np.arange(survey.NFRB): - ils=int(i/10) - if i==0: - plt.plot(dm,eff1s[i],linestyle=':',color='black',label='$\\epsilon_i$') + ils = int(i / 10) + if i == 0: + plt.plot(dm, eff1s[i], linestyle=":", color="black", label="$\\epsilon_i$") else: - plt.plot(dm,eff1s[i],linestyle=':',color='black') - #plt.plot(dm,eff2s[i],linestyle=ls[ils],lw=1) - plt.plot(dm,eff1mean,linewidth=3,color='blue',ls='-',label='$\\bar{\\epsilon}$') - plt.plot(DMx,DMy,'ro',label='${\\rm DM}_i$') - - #plt.plot(dm,eff2mean,linewidth=1,color='black',ls='-') - ncol=int((survey.NFRB+9)/10) - plt.legend(loc='upper right') + plt.plot(dm, eff1s[i], linestyle=":", color="black") + # plt.plot(dm,eff2s[i],linestyle=ls[ils],lw=1) + plt.plot( + dm, eff1mean, linewidth=3, color="blue", ls="-", label="$\\bar{\\epsilon}$" + ) + plt.plot(DMx, DMy, "ro", label="${\\rm DM}_i$") + + # plt.plot(dm,eff2mean,linewidth=1,color='black',ls='-') + ncol = int((survey.NFRB + 9) / 10) + plt.legend(loc="upper right") plt.tight_layout() plt.savefig(savename) plt.close() -def plot_efficiencies(survey,savename='Plots/efficiencies.pdf',showplot=False): + +def plot_efficiencies(survey, savename="Plots/efficiencies.pdf", showplot=False): """ Plots efficiency as function of DM """ plt.figure() - - eff=survey.efficiencies - dm=survey.DMlist + + eff = survey.efficiencies + dm = survey.DMlist if "ID" in survey.frbs: - labels=survey.frbs["ID"] + labels = survey.frbs["ID"] else: - labels=np.arange(survey.NFRB) - - plt.xlabel('DM [pc cm$^{-3}$]') - plt.ylabel('Efficiency $\\epsilon$') - - ls=['-',':','--','-.'] - + labels = np.arange(survey.NFRB) + + plt.xlabel("DM [pc cm$^{-3}$]") + plt.ylabel("Efficiency $\\epsilon$") + + ls = ["-", ":", "--", "-."] + for i in np.arange(survey.NFRB): - ils=int(i/10) - plt.plot(dm,eff[i],label=labels[i],linestyle=ls[ils]) - plt.plot(dm,survey.mean_efficiencies,linewidth=2,color='black',ls='-') - ncol=int((survey.NFRB+9)/10) - plt.legend(loc='upper right',fontsize=min(14,200./survey.NFRB),ncol=ncol) + ils = int(i / 10) + plt.plot(dm, eff[i], label=labels[i], linestyle=ls[ils]) + plt.plot(dm, survey.mean_efficiencies, linewidth=2, color="black", ls="-") + ncol = int((survey.NFRB + 9) / 10) + plt.legend(loc="upper right", fontsize=min(14, 200.0 / survey.NFRB), ncol=ncol) plt.tight_layout() plt.savefig(savename) if showplot: @@ -2390,119 +2966,119 @@ def plot_efficiencies(survey,savename='Plots/efficiencies.pdf',showplot=False): def plot_beams(prefix): - ''' Plots something to do with beams ''' - logb,omega_b=beams.load_beam(prefix) - total=np.sum(omega_b) - print("Total length of histogram is ",omega_b.size) - + """ Plots something to do with beams """ + logb, omega_b = beams.load_beam(prefix) + total = np.sum(omega_b) + print("Total length of histogram is ", omega_b.size) + # rate of -1.5 - b=10**logb - rate=omega_b*b**1.5 - nbins=10 - b2,o2=beams.simplify_beam(logb,omega_b,nbins) - print(b2,o2) - + b = 10 ** logb + rate = omega_b * b ** 1.5 + nbins = 10 + b2, o2 = beams.simplify_beam(logb, omega_b, nbins) + print(b2, o2) + # note that omega_b is just unscaled total solid angle plt.figure() - plt.xlabel('$B$') - plt.ylabel('$\\Omega(B)$/bin') - plt.yscale('log') - plt.xscale('log') - plt.plot(b,omega_b,label='original_binning') - plt.plot(b2,o2,'ro',label='simplified',linestyle=':') - plt.plot(b,rate,label='Relative rate') - plt.legend(loc='upper left') + plt.xlabel("$B$") + plt.ylabel("$\\Omega(B)$/bin") + plt.yscale("log") + plt.xscale("log") + plt.plot(b, omega_b, label="original_binning") + plt.plot(b2, o2, "ro", label="simplified", linestyle=":") + plt.plot(b, rate, label="Relative rate") + plt.legend(loc="upper left") plt.tight_layout() - plt.savefig('Plots/lat50_beam.pdf') + plt.savefig("Plots/lat50_beam.pdf") plt.close() - - - - crate=np.cumsum(rate) + + crate = np.cumsum(rate) crate /= crate[-1] plt.figure() - plt.xlabel('$\\log_{10}(B)$') - plt.ylabel('cumulative rate') - #plt.yscale('log') - plt.plot(logb,crate) + plt.xlabel("$\\log_{10}(B)$") + plt.ylabel("cumulative rate") + # plt.yscale('log') + plt.plot(logb, crate) plt.tight_layout() - plt.savefig('Plots/crate_lat50_beam.pdf') + plt.savefig("Plots/crate_lat50_beam.pdf") plt.close() - crate=np.cumsum(rate) - -def process_missing_pfile(pfile,number,howmany): - ''' searches for missing data in cube output and iterates for that only ''' - NPARAMS=8 - current=np.zeros([1,NPARAMS]) - last=np.zeros([NPARAMS]) - this=np.zeros([NPARAMS]) - n=0 # counts number of lines in this calculation - new=0 # counts new significant calculations - count=0 # counts total lines - + crate = np.cumsum(rate) + + +def process_missing_pfile(pfile, number, howmany): + """ searches for missing data in cube output and iterates for that only """ + NPARAMS = 8 + current = np.zeros([1, NPARAMS]) + last = np.zeros([NPARAMS]) + this = np.zeros([NPARAMS]) + n = 0 # counts number of lines in this calculation + new = 0 # counts new significant calculations + count = 0 # counts total lines + # the range of calculations to do. These are *inclusive* values - start=(number-1)*howmany+1 - stop=number*howmany - print("Calculated start and stop as ",start,stop) - - max_number=np.zeros([howmany*11,NPARAMS]) - - #I am now testing how many in the file in total - + start = (number - 1) * howmany + 1 + stop = number * howmany + print("Calculated start and stop as ", start, stop) + + max_number = np.zeros([howmany * 11, NPARAMS]) + + # I am now testing how many in the file in total + with open(pfile) as pf: - + for line in pf: - #if count==NPARAMS: + # if count==NPARAMS: # break - vals=line.split() - for j,v in enumerate(vals): - this[j]=float(v) - + vals = line.split() + for j, v in enumerate(vals): + this[j] = float(v) + # tests to see if this is a serious calculation - for j in np.arange(NPARAMS-1): - if j==4: + for j in np.arange(NPARAMS - 1): + if j == 4: continue if this[j] != last[j]: new += 1 break - + # tests to see if we have gone far enough if new > stop: break - + # test to see if we now do this if new >= start: - max_number[n,:]=this[:] + max_number[n, :] = this[:] n += 1 - - + # sets last to this one - last[:]=this[:] - + last[:] = this[:] + count += 1 - - if n==0: + + if n == 0: print("Reached the end of the file, exiting") exit() # concatenate max number to true size - todo=max_number[0:n,:] - starti=count-n+1 - return todo,starti + todo = max_number[0:n, :] + starti = count - n + 1 + return todo, starti + def process_pfile(pfile): - ''' used for cube.py to input multi-dimensional grid to iterate over''' - NPARAMS=8 - mins=np.zeros([NPARAMS]) - maxs=np.zeros([NPARAMS]) - Nits=np.zeros([NPARAMS],dtype='int') + """ used for cube.py to input multi-dimensional grid to iterate over""" + NPARAMS = 8 + mins = np.zeros([NPARAMS]) + maxs = np.zeros([NPARAMS]) + Nits = np.zeros([NPARAMS], dtype="int") with open(pfile) as pf: - count=0 + count = 0 for line in pf: - if count==NPARAMS: + if count == NPARAMS: break - vals=line.split() - mins[count]=float(vals[0]) - maxs[count]=float(vals[1]) - Nits[count]=int(vals[2]) + vals = line.split() + mins[count] = float(vals[0]) + maxs[count] = float(vals[1]) + Nits[count] = int(vals[2]) count += 1 - return mins,maxs,Nits + return mins, maxs, Nits + diff --git a/zdm/pcosmic.py b/zdm/pcosmic.py index 9b50889e..f3045011 100644 --- a/zdm/pcosmic.py +++ b/zdm/pcosmic.py @@ -7,7 +7,7 @@ ############################# -from fcntl import F_ADD_SEALS +# from fcntl import F_ADD_SEALS import sys # sys.path.insert(1, '/Users/cjames/CRAFT/FRB_library/ne2001-master/src/ne2001') diff --git a/zdm/scripts/plot_limits_from_cube.py b/zdm/scripts/plot_limits_from_cube.py index fa3580e8..d35d1a6c 100644 --- a/zdm/scripts/plot_limits_from_cube.py +++ b/zdm/scripts/plot_limits_from_cube.py @@ -21,154 +21,195 @@ from matplotlib import pyplot as plt + def main(verbose=False): - + ######### sets the values of H0 for priors ##### Planck_H0 = 67.4 Planck_sigma = 0.5 Reiss_H0 = 74.03 Reiss_sigma = 1.42 - + ##### loads cube data ##### - cube='craco_mini_cube.npz' - data=np.load(cube) + cube = "craco_mini_cube.npz" + data = np.load(cube) if verbose: for thing in data: print(thing) print(data["params"]) - + # gets values of cube parameters - #param_vals=get_param_values(data,verbose) - + # param_vals=get_param_values(data,verbose) + # gets latex names - uvals,latexnames = get_names_values(data) - + uvals, latexnames = get_names_values(data) + ################ single plots, no priors ############ - deprecated,uw_vectors,wvectors=ac.get_bayesian_data(data["ll"]) - ac.do_single_plots(uvals,uw_vectors,None,data["params"],tag="",log=False,logspline=False,kind='linear',truth=None,dolevels=True,latexnames=latexnames) - + deprecated, uw_vectors, wvectors = ac.get_bayesian_data(data["ll"]) + ac.do_single_plots( + uvals, + uw_vectors, + None, + data["params"], + tag="", + log=False, + logspline=False, + kind="linear", + truth=None, + dolevels=True, + latexnames=latexnames, + ) + ########### H0 data for fixed values of other parameters ########### # extracts best-fit values - list1=[] - vals1=[] - list2=[] - vals2=[] - vals3=[] - for i,vec in enumerate(uw_vectors): - n=np.argmax(vec) # selects the most likely value - val=uvals[i][n] + list1 = [] + vals1 = [] + list2 = [] + vals2 = [] + vals3 = [] + for i, vec in enumerate(uw_vectors): + n = np.argmax(vec) # selects the most likely value + val = uvals[i][n] if data["params"][i] == "H0": # enables us to select a slice corresponding to particular H0 values list1.append(data["params"][i]) vals1.append(Reiss_H0) - + vals3.append(Planck_H0) - - iH0=i # setting index for Hubble + + iH0 = i # setting index for Hubble else: # enables us to select a slice correspondng to the best-fit values of all other params # i.e. ignoring uncertainty in them list2.append(data["params"][i]) vals2.append(val) - + # gets the slice corresponding to specific values of H0 - Reiss_H0_selection=ac.get_slice_from_parameters(data,list1,vals1,verbose=True) - Planck_H0_selection=ac.get_slice_from_parameters(data,list1,vals3,verbose=True) - + Reiss_H0_selection = ac.get_slice_from_parameters(data, list1, vals1, verbose=True) + Planck_H0_selection = ac.get_slice_from_parameters(data, list1, vals3, verbose=True) + # will have Bayesian limits on all parameters over everything but H0 - deprecated,ReissH0_vectors,deprecated=ac.get_bayesian_data(Reiss_H0_selection) - deprecated,PlanckH0_vectors,deprecated=ac.get_bayesian_data(Planck_H0_selection) - + deprecated, ReissH0_vectors, deprecated = ac.get_bayesian_data(Reiss_H0_selection) + deprecated, PlanckH0_vectors, deprecated = ac.get_bayesian_data(Planck_H0_selection) + # gets the slice corresponding to the best-fit values of all other parameters # this is 1D, so is our limit on H0 keeping all others fixed - pH0_fixed=ac.get_slice_from_parameters(data,list2,vals2) - + pH0_fixed = ac.get_slice_from_parameters(data, list2, vals2) + ####### 1D plots for prior on H0 ######## # generates plots for our standard prior on H0 only # applies a prior on H0, which is flat between systematic differences, then falls off as a Gaussian either side - H0_dim=np.where(data["params"]=="H0")[0][0] - wlls = ac.apply_H0_prior(data["ll"],H0_dim,data["H0"],Planck_H0, - Planck_sigma, Reiss_H0, Reiss_sigma) - deprecated,wH0_vectors,wvectors=ac.get_bayesian_data(wlls) - ac.do_single_plots(uvals,wH0_vectors,None,data["params"],tag="wH0_",truth=None, - dolevels=True,latexnames=latexnames,logspline=False) - - + H0_dim = np.where(data["params"] == "H0")[0][0] + wlls = ac.apply_H0_prior( + data["ll"], H0_dim, data["H0"], Planck_H0, Planck_sigma, Reiss_H0, Reiss_sigma + ) + deprecated, wH0_vectors, wvectors = ac.get_bayesian_data(wlls) + ac.do_single_plots( + uvals, + wH0_vectors, + None, + data["params"], + tag="wH0_", + truth=None, + dolevels=True, + latexnames=latexnames, + logspline=False, + ) + # now do this with others... # builds others... - others=[] - for i,p in enumerate(data["params"]): - if i==iH0: - oset=None + others = [] + for i, p in enumerate(data["params"]): + if i == iH0: + oset = None others.append(oset) else: - if i Date: Thu, 29 Sep 2022 21:21:19 -0400 Subject: [PATCH 052/104] add mini logF yaml --- .../CRACO/Cloud/nautilus_craco_mini_logF.yaml | 80 +++++++++++++++++++ 1 file changed, 80 insertions(+) create mode 100644 papers/F/Analysis/CRACO/Cloud/nautilus_craco_mini_logF.yaml diff --git a/papers/F/Analysis/CRACO/Cloud/nautilus_craco_mini_logF.yaml b/papers/F/Analysis/CRACO/Cloud/nautilus_craco_mini_logF.yaml new file mode 100644 index 00000000..eee30a1d --- /dev/null +++ b/papers/F/Analysis/CRACO/Cloud/nautilus_craco_mini_logF.yaml @@ -0,0 +1,80 @@ +# 25 processors on mini for Varying F +# kubectl exec -it test-pod -- /bin/bash +apiVersion: batch/v1 +kind: Job +metadata: + name: jay-zdm-craco-mini-logf +spec: + backoffLimit: 0 + template: + spec: + affinity: + nodeAffinity: + requiredDuringSchedulingIgnoredDuringExecution: + nodeSelectorTerms: + - matchExpressions: + - key: kubernetes.io/hostname + operator: NotIn + values: + - k8s-chase-ci-01.noc.ucsb.edu + - key: nvidia.com/gpu.product + operator: In + values: + - NVIDIA-GeForce-GTX-1080-Ti + containers: + - name: container + image: localhost:30081/profxj/zdm_docker:latest # UPDATE + imagePullPolicy: Always + resources: + requests: + cpu: "25" + memory: "8Gi" # + ephemeral-storage: 50Gi # + limits: + cpu: "27" + memory: "12Gi" + ephemeral-storage: 100Gi + #nvidia.com/gpu: "1" # See docs to exlude certain types + command: ["/bin/bash", "-c"] + args: + - cd FRB; + git fetch; + git pull; + python setup.py develop; + cd ../ne2001; + python setup.py develop; + cd ../zdm; + git fetch; + git checkout logF; + python setup.py develop; + cd papers/F/Analysis/CRACO/Cloud; + python run_craco_mini_logF.py -n 25 -t 25 -b 1; + aws --endpoint http://rook-ceph-rgw-nautiluss3.rook s3 cp Output s3://zdm/Cubes/F/mini/ --recursive --force; + env: + - name: "ENDPOINT_URL" + value: "http://rook-ceph-rgw-nautiluss3.rook" + - name: "S3_ENDPOINT" + value: "rook-ceph-rgw-nautiluss3.rook" + volumeMounts: + - name: prp-s3-credentials + mountPath: "/root/.aws/credentials" + subPath: "credentials" + - name: ephemeral + mountPath: "/tmp" + - name: "dshm" + mountPath: "/dev/shm" + nodeSelector: + nautilus.io/disktype: nvme + restartPolicy: Never + volumes: + # Secrets file for nautilus s3 credentials .aws/credentials and .s3cfg + - name: prp-s3-credentials + secret: + secretName: prp-s3-credentials + # Shared memory (necessary for Python's multiprocessing.shared_memory module to work) + - name: dshm + emptyDir: + medium: Memory + # Ephemeral storage + - name: ephemeral + emptyDir: {} From 78c898d0f93927f20951dac34307d22dfd25a3b9 Mon Sep 17 00:00:00 2001 From: Jay-MBP Date: Sat, 1 Oct 2022 12:36:46 -0400 Subject: [PATCH 053/104] switch to faster luminosity function --- papers/F/Analysis/CRACO/Cubes/craco_H0_F_cube.json | 2 +- papers/F/Analysis/CRACO/Cubes/craco_H0_logF_cube.json | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/papers/F/Analysis/CRACO/Cubes/craco_H0_F_cube.json b/papers/F/Analysis/CRACO/Cubes/craco_H0_F_cube.json index 5ffbb63c..752075cf 100644 --- a/papers/F/Analysis/CRACO/Cubes/craco_H0_F_cube.json +++ b/papers/F/Analysis/CRACO/Cubes/craco_H0_F_cube.json @@ -1,7 +1,7 @@ { "state": { "energy": { - "luminosity_function": 2 + "luminosity_function": 3 }, "FRBdemo": { "alpha_method": 1 diff --git a/papers/F/Analysis/CRACO/Cubes/craco_H0_logF_cube.json b/papers/F/Analysis/CRACO/Cubes/craco_H0_logF_cube.json index 1c4da32b..f38e519c 100644 --- a/papers/F/Analysis/CRACO/Cubes/craco_H0_logF_cube.json +++ b/papers/F/Analysis/CRACO/Cubes/craco_H0_logF_cube.json @@ -1,7 +1,7 @@ { "state": { "energy": { - "luminosity_function": 2 + "luminosity_function": 3 }, "FRBdemo": { "alpha_method": 1 From 80aa4fdc597d31e2f9ef1f152412d213f074031a Mon Sep 17 00:00:00 2001 From: Jay-MBP Date: Sat, 1 Oct 2022 12:38:19 -0400 Subject: [PATCH 054/104] set lf=3 on minicube run --- papers/F/Analysis/CRACO/Cubes/craco_mini_cube_logF.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/papers/F/Analysis/CRACO/Cubes/craco_mini_cube_logF.json b/papers/F/Analysis/CRACO/Cubes/craco_mini_cube_logF.json index b03b639b..3a9fdde2 100644 --- a/papers/F/Analysis/CRACO/Cubes/craco_mini_cube_logF.json +++ b/papers/F/Analysis/CRACO/Cubes/craco_mini_cube_logF.json @@ -1,7 +1,7 @@ { "state": { "energy": { - "luminosity_function": 2 + "luminosity_function": 3 }, "FRBdemo": { "alpha_method": 1 From c73863ff6301644fab31a3ee2e14b4a9a14df621 Mon Sep 17 00:00:00 2001 From: profxj Date: Mon, 3 Oct 2022 11:44:43 -0700 Subject: [PATCH 055/104] edits --- papers/F/Analysis/CRACO/Cubes/craco_mini_cube.json | 4 ++-- zdm/grid.py | 2 ++ 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/papers/F/Analysis/CRACO/Cubes/craco_mini_cube.json b/papers/F/Analysis/CRACO/Cubes/craco_mini_cube.json index cedf3118..03ab4f3c 100644 --- a/papers/F/Analysis/CRACO/Cubes/craco_mini_cube.json +++ b/papers/F/Analysis/CRACO/Cubes/craco_mini_cube.json @@ -14,11 +14,11 @@ "parameter_order": [ "lC", "sfr_n", - "alpha", - "lEmax", "lmean", "lsigma", "F", + "alpha", + "lEmax", "gamma", "H0" ] diff --git a/zdm/grid.py b/zdm/grid.py index fec68da8..cf7b4552 100644 --- a/zdm/grid.py +++ b/zdm/grid.py @@ -656,12 +656,14 @@ def update(self, vparams: dict, ALL=False, prev_grid=None): Emin Emax gamma + H0 calc_thresholds F0 alpha bandwidth set_evolution sfr_n + H0 smear_grid grid From 27193259e04a3f04fe040d49df320e93ea4541e0 Mon Sep 17 00:00:00 2001 From: Jay Baptista Date: Tue, 18 Oct 2022 14:21:20 -0400 Subject: [PATCH 056/104] pzdm figs compatible with logF; slim full cube --- .../Analysis/CRACO/Cubes/craco_full_cube.json | 52 +++++++ papers/F/Figures/py/figs_zdm_F_I.py | 130 ++++++++++-------- 2 files changed, 126 insertions(+), 56 deletions(-) create mode 100644 papers/F/Analysis/CRACO/Cubes/craco_full_cube.json diff --git a/papers/F/Analysis/CRACO/Cubes/craco_full_cube.json b/papers/F/Analysis/CRACO/Cubes/craco_full_cube.json new file mode 100644 index 00000000..8f2ae9f0 --- /dev/null +++ b/papers/F/Analysis/CRACO/Cubes/craco_full_cube.json @@ -0,0 +1,52 @@ +{ + "state": { + "energy": { + "luminosity_function": 3 + }, + "FRBdemo": { + "alpha_method": 1 + }, + "cosmo": { + "fix_Omega_b_h2": true + } + }, + "cube": { + "parameter_order": [ + "lC", + "lmean", + "lsigma", + "logF", + "H0" + ] + }, + "H0": { + "DC": "cosmo", + "min": 60.0, + "max": 75.0, + "n": 16 + }, + "lmean": { + "DC": "host", + "min": 1.7, + "max": 2.5, + "n": 10 + }, + "lsigma": { + "DC": "host", + "min": 0.2, + "max": 0.9, + "n": 10 + }, + "logF": { + "DC": "IGM", + "min": -1.7, + "max": 0, + "n": 20 + }, + "lC": { + "DC": "FRBdemo", + "min": -0.911, + "max": -0.911, + "n": -1 + } +} \ No newline at end of file diff --git a/papers/F/Figures/py/figs_zdm_F_I.py b/papers/F/Figures/py/figs_zdm_F_I.py index 4c7a414a..849f67d2 100644 --- a/papers/F/Figures/py/figs_zdm_F_I.py +++ b/papers/F/Figures/py/figs_zdm_F_I.py @@ -363,7 +363,7 @@ def fig_craco_fiducial_F( fiducial_H0 = grid.state.cosmo.H0 - vparams = {"H0": fiducial_H0, "F": F} + vparams = {"H0": fiducial_H0, "logF": F} if H0 is not None: vparams["H0"] = H0 @@ -426,7 +426,7 @@ def fig_craco_fiducial_F( ax = plt.gca() - ax.set_title(f"F = {F}") + ax.set_title(rf"$\log F = {F}$") muDMhost = np.log(10 ** grid.state.host.lmean) sigmaDMhost = np.log(10 ** grid.state.host.lsigma) @@ -504,67 +504,85 @@ def fig_craco_fiducial_F( ### tests -fig_craco_varyF_zDM("contours_varyF_H0.pdf", other_param="H0") -fig_craco_varyF_zDM( - "contours_varyF_H0_dmhost_suppressed.pdf", other_param="H0", suppress_DM_host=True -) - -fig_craco_fiducial_F( - "fig_craco_F_0.32_dmhost_suppressed.png", - show_Macquart=True, - F=0.32, - suppress_DM_host=True, -) -fig_craco_fiducial_F( - "fig_craco_F_0.01_dmhost_suppressed.png", - show_Macquart=True, - F=0.01, - suppress_DM_host=True, -) -fig_craco_fiducial_F( - "fig_craco_F_0.9_dmhost_suppressed.png", - show_Macquart=True, - F=0.9, - suppress_DM_host=True, -) - -fig_craco_fiducial_F( - "fig_craco_F_0.32.png", show_Macquart=False, F=0.32, suppress_DM_host=False -) +# fig_craco_fiducial_F( +# "fig_craco_logF_-.5_H0_56.png", +# show_Macquart=False, +# F=-0.5, +# H0=56, +# suppress_DM_host=False, +# ) fig_craco_fiducial_F( - "fig_craco_F_0.82_H0_55.png", + "fig_craco_logF_-1.51_H0_56.png", show_Macquart=False, - F=0.82, - H0=55.0, + F=-1.51, + H0=56, suppress_DM_host=False, ) -fig_craco_fiducial_F( - "fig_craco_F_0.01.png", show_Macquart=True, F=0.01, suppress_DM_host=False -) -fig_craco_fiducial_F( - "fig_craco_F_0.9.png", show_Macquart=True, F=0.9, suppress_DM_host=False -) -fig_varyF( - "fig_lmean_degeneracy_varyF.png", - other_param="lmean", - F_values=[0.01, 0.9], - other_values=[None, None], - lcolors=["r", "b"], - lstyles=["-", "-"], - DMmax=1800, -) +#### -fig_varyF( - "fig_lmean_degeneracy_varylm.png", - other_param="lmean", - F_values=[None, None], - other_values=[2.5, 1.5], - lcolors=["#e07a5f", "#81b29a"], - lstyles=["-", "-"], - DMmax=1800, -) +# fig_craco_varyF_zDM("contours_varyF_H0.pdf", other_param="H0") +# fig_craco_varyF_zDM( +# "contours_varyF_H0_dmhost_suppressed.pdf", other_param="H0", suppress_DM_host=True +# ) + +# fig_craco_fiducial_F( +# "fig_craco_F_0.32_dmhost_suppressed.png", +# show_Macquart=True, +# F=0.32, +# suppress_DM_host=True, +# ) +# fig_craco_fiducial_F( +# "fig_craco_F_0.01_dmhost_suppressed.png", +# show_Macquart=True, +# F=0.01, +# suppress_DM_host=True, +# ) +# fig_craco_fiducial_F( +# "fig_craco_F_0.9_dmhost_suppressed.png", +# show_Macquart=True, +# F=0.9, +# suppress_DM_host=True, +# ) + +# fig_craco_fiducial_F( +# "fig_craco_F_0.32.png", show_Macquart=False, F=0.32, suppress_DM_host=False +# ) + +# fig_craco_fiducial_F( +# "fig_craco_F_0.82_H0_55.png", +# show_Macquart=False, +# F=0.82, +# H0=55.0, +# suppress_DM_host=False, +# ) +# fig_craco_fiducial_F( +# "fig_craco_F_0.01.png", show_Macquart=True, F=0.01, suppress_DM_host=False +# ) +# fig_craco_fiducial_F( +# "fig_craco_F_0.9.png", show_Macquart=True, F=0.9, suppress_DM_host=False +# ) + +# fig_varyF( +# "fig_lmean_degeneracy_varyF.png", +# other_param="lmean", +# F_values=[0.01, 0.9], +# other_values=[None, None], +# lcolors=["r", "b"], +# lstyles=["-", "-"], +# DMmax=1800, +# ) + +# fig_varyF( +# "fig_lmean_degeneracy_varylm.png", +# other_param="lmean", +# F_values=[None, None], +# other_values=[2.5, 1.5], +# lcolors=["#e07a5f", "#81b29a"], +# lstyles=["-", "-"], +# DMmax=1800, +# ) # fig_varyF( # "test.png", From 59963ea922b41678887c2e2bbcea39e77ba2fd4b Mon Sep 17 00:00:00 2001 From: Jay Baptista Date: Wed, 19 Oct 2022 13:14:32 -0400 Subject: [PATCH 057/104] undo black formatting on grid.py --- zdm/grid.py | 20 +++----------------- 1 file changed, 3 insertions(+), 17 deletions(-) diff --git a/zdm/grid.py b/zdm/grid.py index 01db5134..4890c0f3 100644 --- a/zdm/grid.py +++ b/zdm/grid.py @@ -267,9 +267,7 @@ def calc_pdv(self, beam_b=None, beam_o=None): # call log10 beam if self.use_log10: - new_thresh = np.log10( - self.thresholds - ) # use when calling in log10 space conversion + new_thresh = np.log10(self.thresholds) # use when calling in log10 space conversion main_beam_b = np.log10(main_beam_b) for i, b in enumerate(main_beam_b): @@ -282,21 +280,9 @@ def calc_pdv(self, beam_b=None, beam_o=None): thresh = self.thresholds[j, :, :] / b if j == 0: - self.b_fractions[:, :, i] = ( - self.beam_o[i] - * w - * self.array_cum_lf( - thresh, Emin, Emax, self.state.energy.gamma, self.use_log10 - ) - ) + self.b_fractions[:, :, i] = (self.beam_o[i] * w * self.array_cum_lf(thresh, Emin, Emax, self.state.energy.gamma, self.use_log10)) else: - self.b_fractions[:, :, i] += ( - self.beam_o[i] - * w - * self.array_cum_lf( - thresh, Emin, Emax, self.state.energy.gamma, self.use_log10 - ) - ) + self.b_fractions[:, :, i] += (self.beam_o[i] * w * self.array_cum_lf(thresh, Emin, Emax, self.state.energy.gamma, self.use_log10)) # here, b-fractions are unweighted according to the value of b. self.fractions = np.sum( From e7ed640df0194db5c46cc59d0271efb1b08f7300 Mon Sep 17 00:00:00 2001 From: Jay Baptista Date: Wed, 19 Oct 2022 13:20:09 -0400 Subject: [PATCH 058/104] fixing the formatting one last time --- zdm/grid.py | 12 ++++++++---- 1 file changed, 8 insertions(+), 4 deletions(-) diff --git a/zdm/grid.py b/zdm/grid.py index 4890c0f3..f64ac0a1 100644 --- a/zdm/grid.py +++ b/zdm/grid.py @@ -279,11 +279,15 @@ def calc_pdv(self, beam_b=None, beam_o=None): else: # original thresh = self.thresholds[j, :, :] / b - if j == 0: - self.b_fractions[:, :, i] = (self.beam_o[i] * w * self.array_cum_lf(thresh, Emin, Emax, self.state.energy.gamma, self.use_log10)) + if j==0: + self.b_fractions[:,:,i] = self.beam_o[i]*w*self.array_cum_lf( + thresh,Emin,Emax, + self.state.energy.gamma, self.use_log10) else: - self.b_fractions[:, :, i] += (self.beam_o[i] * w * self.array_cum_lf(thresh, Emin, Emax, self.state.energy.gamma, self.use_log10)) - + self.b_fractions[:,:,i] += self.beam_o[i]*w*self.array_cum_lf( + thresh,Emin,Emax, + self.state.energy.gamma, self.use_log10) + # here, b-fractions are unweighted according to the value of b. self.fractions = np.sum( self.b_fractions, axis=2 From a71781bb505a99185111c1e0a3c98c30d2c4ba59 Mon Sep 17 00:00:00 2001 From: Jay Baptista Date: Wed, 19 Oct 2022 13:30:32 -0400 Subject: [PATCH 059/104] adapt unit test for logF --- papers/H0_I/Figures/py/figs_zdm_H0_I.py | 14 +++++++------- zdm/scripts/plot_grid_components.py | 6 +++--- zdm/tests/Performance/test_update_performance.py | 4 ++-- 3 files changed, 12 insertions(+), 12 deletions(-) diff --git a/papers/H0_I/Figures/py/figs_zdm_H0_I.py b/papers/H0_I/Figures/py/figs_zdm_H0_I.py index 587b987b..af167536 100644 --- a/papers/H0_I/Figures/py/figs_zdm_H0_I.py +++ b/papers/H0_I/Figures/py/figs_zdm_H0_I.py @@ -222,7 +222,7 @@ def fig_craco_varyH0_zDM(outfile, lstyles = ['-', '-', '-', ':'] zticks = [0.5, 1.0, 1.5, 2.] ylim = (0., DMmax) - elif other_param == 'F': + elif other_param == 'logF': H0_values = [60., 70., 80., 60.] other_values = [fiducial_F, fiducial_F, fiducial_F, 0.5] lstyle = '-' @@ -242,8 +242,8 @@ def fig_craco_varyH0_zDM(outfile, vparams['H0'] = H0 if other_param == 'Emax': vparams['lEmax'] = fiducial_Emax + scl - elif other_param == 'F': - vparams['F'] = scl + elif other_param == 'logF': + vparams['logF'] = scl grid.update(vparams) # Unpack @@ -279,8 +279,8 @@ def fig_craco_varyH0_zDM(outfile, # Label if other_param == 'Emax': labels.append(r"$H_0 = $"+f"{H0}, log "+r"$E_{\rm max}$"+f"= {vparams['lEmax']}") - elif other_param == 'F': - labels.append(r"$H_0 = $"+f"{H0}, F = {vparams['F']}") + elif other_param == 'logF': + labels.append(r"$H_0 = $"+f"{H0}, F = {vparams['logF']}") ###### gets decent axis labels, down to 1 decimal place ####### ax=plt.gca() @@ -327,7 +327,7 @@ def fig_craco_varyH0_other(outfile, params, H0_values = [60., 70., 80., 80.] other_values = [41.4, 41.4, 41.4, 41.3] lstyles = ['-', '-', '-', ':'] - elif other_param == 'F': + elif other_param == 'logF': H0_values = [60., 70., 80., 60.] other_values = [fiducial_F, fiducial_F, fiducial_F, 0.5] lstyle = '-' @@ -412,7 +412,7 @@ def fig_craco_varyH0_other(outfile, params, if other_param == "Emax": labels.append(r"$H_0 = $" + f"{H0}, log " + r"$E_{\rm max}$" + f"= {lEmax}") elif other_param == "F": - labels.append(r"$H_0 = $" + f"{H0}, F = {vparams['F']}") + labels.append(r"$H_0 = $" + f"{H0}, F = {vparams['logF']}") ###### gets decent axis labels, down to 1 decimal place ####### ax = plt.gca() diff --git a/zdm/scripts/plot_grid_components.py b/zdm/scripts/plot_grid_components.py index 49648b06..cb76c293 100644 --- a/zdm/scripts/plot_grid_components.py +++ b/zdm/scripts/plot_grid_components.py @@ -47,10 +47,10 @@ def main(): H0=80 - F=0.32 + logF=np.log10(0.32) # in case you wish to switch to another output directory - opdir='GridComponents_H'+str(H0)+'_F'+str(F)+'/' + opdir='GridComponents_H'+str(H0)+'_logF'+str(logF)+'/' if not os.path.exists(opdir): os.mkdir(opdir) @@ -61,7 +61,7 @@ def main(): # approximate best-fit values from recent analysis vparams = {} vparams['H0'] = H0 #real one is 73 - vparams['F'] = F + vparams['logF'] = logF vparams['lEmax'] = 41.3 vparams['gamma'] = -0.9 vparams['alpha'] = 1 diff --git a/zdm/tests/Performance/test_update_performance.py b/zdm/tests/Performance/test_update_performance.py index 44f152d9..b21c1fe8 100644 --- a/zdm/tests/Performance/test_update_performance.py +++ b/zdm/tests/Performance/test_update_performance.py @@ -127,9 +127,9 @@ def test_performance(likelihoods=True,detail=0,verbose=True): # retrieves a state state=grids[0].state #list of parameters to vary - plist=['H0','F','lEmin','lEmax','gamma','alpha','sfr_n','lmean','lsigma'] + plist=['H0','logF','lEmin','lEmax','gamma','alpha','sfr_n','lmean','lsigma'] # original values for the loop - ovals=[state.cosmo.H0,state.IGM.F, + ovals=[state.cosmo.H0,state.IGM.logF, state.energy.lEmin,state.energy.lEmax,state.energy.gamma,state.energy.alpha, state.FRBdemo.sfr_n, state.host.lmean,state.host.lsigma] From 3096c7c7b2a7d701ea9ba3e454aea948b4c910e2 Mon Sep 17 00:00:00 2001 From: Jay Baptista Date: Wed, 19 Oct 2022 13:41:49 -0400 Subject: [PATCH 060/104] fix test json files --- zdm/tests/files/craco_H0_Emax_state.json | 2 +- zdm/tests/files/scat_test_new.json | 4 ++-- zdm/tests/files/scat_test_old.json | 4 ++-- 3 files changed, 5 insertions(+), 5 deletions(-) diff --git a/zdm/tests/files/craco_H0_Emax_state.json b/zdm/tests/files/craco_H0_Emax_state.json index 0efc21a7..f5f20b13 100644 --- a/zdm/tests/files/craco_H0_Emax_state.json +++ b/zdm/tests/files/craco_H0_Emax_state.json @@ -6,7 +6,7 @@ "source_evolution": 0 }, "IGM": { - "F": 0.32 + "logF": -0.49485 }, "MW": { "DMhalo": 50, diff --git a/zdm/tests/files/scat_test_new.json b/zdm/tests/files/scat_test_new.json index 6a604e2c..a8148fe1 100644 --- a/zdm/tests/files/scat_test_new.json +++ b/zdm/tests/files/scat_test_new.json @@ -6,7 +6,7 @@ "source_evolution": 0 }, "IGM": { - "F": 0.32 + "logF": -0.49485 }, "MW": { "DMhalo": 50, @@ -54,4 +54,4 @@ "Sfnorm": 600, "Sfpower": -4 } -} +} \ No newline at end of file diff --git a/zdm/tests/files/scat_test_old.json b/zdm/tests/files/scat_test_old.json index 35fcee09..6b661134 100644 --- a/zdm/tests/files/scat_test_old.json +++ b/zdm/tests/files/scat_test_old.json @@ -6,7 +6,7 @@ "source_evolution": 0 }, "IGM": { - "F": 0.32 + "logF": -0.49485 }, "MW": { "DMhalo": 50, @@ -54,4 +54,4 @@ "Sfnorm": 600, "Sfpower": -4 } -} +} \ No newline at end of file From 105c86c63df3d65cf33856ec802cef62a66f36b1 Mon Sep 17 00:00:00 2001 From: profxj Date: Wed, 19 Oct 2022 11:52:37 -0700 Subject: [PATCH 061/104] upping H0, F --- papers/F/Analysis/CRACO/Cubes/craco_full_cube.json | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/papers/F/Analysis/CRACO/Cubes/craco_full_cube.json b/papers/F/Analysis/CRACO/Cubes/craco_full_cube.json index 8f2ae9f0..929506d8 100644 --- a/papers/F/Analysis/CRACO/Cubes/craco_full_cube.json +++ b/papers/F/Analysis/CRACO/Cubes/craco_full_cube.json @@ -22,8 +22,8 @@ "H0": { "DC": "cosmo", "min": 60.0, - "max": 75.0, - "n": 16 + "max": 80.0, + "n": 21 }, "lmean": { "DC": "host", @@ -41,7 +41,7 @@ "DC": "IGM", "min": -1.7, "max": 0, - "n": 20 + "n": 30 }, "lC": { "DC": "FRBdemo", From 62edfc2717e60ea02b039675ddf676a8b6d355fa Mon Sep 17 00:00:00 2001 From: profxj Date: Wed, 19 Oct 2022 11:54:36 -0700 Subject: [PATCH 062/104] full --- .../CRACO/Cloud/nautilus_craco_full_logF.yaml | 80 +++++++++++ .../CRACO/Cloud/run_craco_full_logF.py | 130 ++++++++++++++++++ 2 files changed, 210 insertions(+) create mode 100644 papers/F/Analysis/CRACO/Cloud/nautilus_craco_full_logF.yaml create mode 100644 papers/F/Analysis/CRACO/Cloud/run_craco_full_logF.py diff --git a/papers/F/Analysis/CRACO/Cloud/nautilus_craco_full_logF.yaml b/papers/F/Analysis/CRACO/Cloud/nautilus_craco_full_logF.yaml new file mode 100644 index 00000000..18c8d930 --- /dev/null +++ b/papers/F/Analysis/CRACO/Cloud/nautilus_craco_full_logF.yaml @@ -0,0 +1,80 @@ +# 21 processors on full for Varying F +# kubectl exec -it test-pod -- /bin/bash +apiVersion: batch/v1 +kind: Job +metadata: + name: x-zdm-craco-full-logf +spec: + backoffLimit: 0 + template: + spec: + affinity: + nodeAffinity: + requiredDuringSchedulingIgnoredDuringExecution: + nodeSelectorTerms: + - matchExpressions: + - key: kubernetes.io/hostname + operator: NotIn + values: + - k8s-chase-ci-01.noc.ucsb.edu + - key: nvidia.com/gpu.product + operator: In + values: + - NVIDIA-GeForce-GTX-1080-Ti + containers: + - name: container + image: localhost:30081/profxj/zdm_docker:latest # UPDATE + imagePullPolicy: Always + resources: + requests: + cpu: "21" + memory: "8Gi" # + ephemeral-storage: 50Gi # + limits: + cpu: "23" + memory: "12Gi" + ephemeral-storage: 100Gi + #nvidia.com/gpu: "1" # See docs to exlude certain types + command: ["/bin/bash", "-c"] + args: + - cd FRB; + git fetch; + git pull; + python setup.py develop; + cd ../ne2001; + python setup.py develop; + cd ../zdm; + git fetch; + git checkout logF; + python setup.py develop; + cd papers/F/Analysis/CRACO/Cloud; + python run_craco_full_logF.py -n 21 -t 21 -b 1; + aws --endpoint http://rook-ceph-rgw-nautiluss3.rook s3 cp Output s3://zdm/Cubes/F/full/ --recursive --force; + env: + - name: "ENDPOINT_URL" + value: "http://rook-ceph-rgw-nautiluss3.rook" + - name: "S3_ENDPOINT" + value: "rook-ceph-rgw-nautiluss3.rook" + volumeMounts: + - name: prp-s3-credentials + mountPath: "/root/.aws/credentials" + subPath: "credentials" + - name: ephemeral + mountPath: "/tmp" + - name: "dshm" + mountPath: "/dev/shm" + nodeSelector: + nautilus.io/disktype: nvme + restartPolicy: Never + volumes: + # Secrets file for nautilus s3 credentials .aws/credentials and .s3cfg + - name: prp-s3-credentials + secret: + secretName: prp-s3-credentials + # Shared memory (necessary for Python's multiprocessing.shared_memory module to work) + - name: dshm + emptyDir: + medium: Memory + # Ephemeral storage + - name: ephemeral + emptyDir: {} diff --git a/papers/F/Analysis/CRACO/Cloud/run_craco_full_logF.py b/papers/F/Analysis/CRACO/Cloud/run_craco_full_logF.py new file mode 100644 index 00000000..47b9df97 --- /dev/null +++ b/papers/F/Analysis/CRACO/Cloud/run_craco_full_logF.py @@ -0,0 +1,130 @@ +""" Run a Nautilus test """ + +# It should be possible to remove all the matplotlib calls from this +# but in the current implementation it is not removed. +import argparse +import numpy as np +import os, sys +from pkg_resources import resource_filename + +from concurrent.futures import ProcessPoolExecutor +import subprocess + +from zdm import iteration as it +from zdm import io + +from IPython import embed + + +def main( + pargs, + pfile: str, + oproot: str, + NFRB: int = None, + iFRB: int = 0, + outdir: str = "Output", +): + + # Generate the folder? + if not os.path.isdir(outdir): + os.mkdir(outdir) + + ############## Load up ############## + input_dict = io.process_jfile(pfile) + + # Deconstruct the input_dict + state_dict, cube_dict, vparam_dict = it.parse_input_dict(input_dict) + + npoints = np.array([item["n"] for key, item in vparam_dict.items()]) + ntotal = int(np.prod(np.abs(npoints))) + + # Total number of CPUs to be running on this Cube + total_ncpu = pargs.ncpu if pargs.total_ncpu is None else pargs.total_ncpu + batch = 1 if pargs.batch is None else pargs.batch + + nper_cpu = ntotal // total_ncpu + if int(ntotal / total_ncpu) != nper_cpu: + raise IOError(f"Ncpu={total_ncpu} must divide evenly into ntotal={ntotal}") + + survey_file = os.path.join(resource_filename('zdm', 'craco'), + 'MC_F', 'Surveys', 'F_0.32_survey') + commands = [] + for kk in range(pargs.ncpu): + line = [] + # Which CPU is running out of the total? + iCPU = (batch - 1) * pargs.ncpu + kk + outfile = os.path.join(outdir, oproot.replace(".csv", f"{iCPU+1}.csv")) + # Command + line = [ + "zdm_build_cube", + "-n", + f"{iCPU+1}", + "-m", + f"{nper_cpu}", + "-o", + f"{outfile}", + "-s", + f"{survey_file}", + "--clobber", + "-p", + f"{pfile}", + ] + # NFRB? + if NFRB is not None: + line += [f"--NFRB", f"{NFRB}"] + # iFRB? + if iFRB > 0: + line += [f"--iFRB", f"{iFRB}"] + # Finish + # line += ' & \n' + commands.append(line) + + # Launch em! + processes = [] + + for command in commands: + # Popen + print(f"Running this command: {' '.join(command)}") + pw = subprocess.Popen(command) + processes.append(pw) + + # Wait on em! + for pw in processes: + pw.wait() + + print("All done!") + + +def parse_option(): + # test for command-line arguments here + parser = argparse.ArgumentParser() + parser.add_argument( + "-n", + "--ncpu", + type=int, + required=True, + help="Number of CPUs to run on (might be split in batches)", + ) + parser.add_argument( + "-t", + "--total_ncpu", + type=int, + required=False, + help="Total number of CPUs to run on (might be split in batches)", + ) + parser.add_argument( + "-b", "--batch", type=int, default=1, required=False, help="Batch number" + ) + # parser.add_argument('--NFRB',type=int,required=False,help="Number of FRBs to analzye") + # parser.add_argument('--iFRB',type=int,default=0,help="Initial FRB to run from") + args = parser.parse_args() + + return args + + +if __name__ == "__main__": + # get the argument of training. + pfile = "../Cubes/craco_full_cube_logF.json" + oproot = "craco_full.csv" + pargs = parse_option() + main(pargs, pfile, oproot, NFRB=100, iFRB=100) From 77c57f61b6f45e625613995c062ee6c0ba5c73ea Mon Sep 17 00:00:00 2001 From: profxj Date: Wed, 19 Oct 2022 12:08:28 -0700 Subject: [PATCH 063/104] bug fix --- papers/F/Analysis/CRACO/Cloud/nautilus_craco_full_logF.yaml | 3 ++- papers/F/Analysis/CRACO/Cloud/run_craco_full_logF.py | 2 +- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/papers/F/Analysis/CRACO/Cloud/nautilus_craco_full_logF.yaml b/papers/F/Analysis/CRACO/Cloud/nautilus_craco_full_logF.yaml index 18c8d930..f0d6f971 100644 --- a/papers/F/Analysis/CRACO/Cloud/nautilus_craco_full_logF.yaml +++ b/papers/F/Analysis/CRACO/Cloud/nautilus_craco_full_logF.yaml @@ -45,9 +45,10 @@ spec: python setup.py develop; cd ../zdm; git fetch; - git checkout logF; + git checkout varying_F; python setup.py develop; cd papers/F/Analysis/CRACO/Cloud; + mkdir Output; python run_craco_full_logF.py -n 21 -t 21 -b 1; aws --endpoint http://rook-ceph-rgw-nautiluss3.rook s3 cp Output s3://zdm/Cubes/F/full/ --recursive --force; env: diff --git a/papers/F/Analysis/CRACO/Cloud/run_craco_full_logF.py b/papers/F/Analysis/CRACO/Cloud/run_craco_full_logF.py index 47b9df97..fbc185bd 100644 --- a/papers/F/Analysis/CRACO/Cloud/run_craco_full_logF.py +++ b/papers/F/Analysis/CRACO/Cloud/run_craco_full_logF.py @@ -124,7 +124,7 @@ def parse_option(): if __name__ == "__main__": # get the argument of training. - pfile = "../Cubes/craco_full_cube_logF.json" + pfile = "../Cubes/craco_full_cube.json" oproot = "craco_full.csv" pargs = parse_option() main(pargs, pfile, oproot, NFRB=100, iFRB=100) From b1863567d6de60047bbbfa9afbc5676c6a8d3238 Mon Sep 17 00:00:00 2001 From: Jay Baptista Date: Wed, 19 Oct 2022 17:45:38 -0400 Subject: [PATCH 064/104] modify slurp scripts for full run --- papers/F/Analysis/CRACO/py/craco_qck_explore.py | 3 +++ papers/F/Analysis/CRACO/py/slurp_craco_cubes.py | 11 +++++++++++ 2 files changed, 14 insertions(+) diff --git a/papers/F/Analysis/CRACO/py/craco_qck_explore.py b/papers/F/Analysis/CRACO/py/craco_qck_explore.py index bdf28c25..1aab71fd 100644 --- a/papers/F/Analysis/CRACO/py/craco_qck_explore.py +++ b/papers/F/Analysis/CRACO/py/craco_qck_explore.py @@ -28,6 +28,9 @@ def main(pargs): elif pargs.run == "H0_logF": scube = "H0_logF" outdir = "H0_logF/" + elif pargs.run == "logF_full": + scube = "full" + outdir = "logF_Full/" elif pargs.run == "full": scube = "full" outdir = "Full/" diff --git a/papers/F/Analysis/CRACO/py/slurp_craco_cubes.py b/papers/F/Analysis/CRACO/py/slurp_craco_cubes.py index 72fc1d72..a4023838 100644 --- a/papers/F/Analysis/CRACO/py/slurp_craco_cubes.py +++ b/papers/F/Analysis/CRACO/py/slurp_craco_cubes.py @@ -38,6 +38,17 @@ def main(pargs): input_file, prefix, "Cubes/craco_H0_logF_cube.npz", nsurveys ) + elif pargs.run == "logF_full": + # Emax + input_file = "Cubes/craco_full_cube.json" + prefix = "Cloud/OutputFull/craco_full" + nsurveys = 1 + + # Run it + analyze_cube.slurp_cube( + input_file, prefix, "Cubes/craco_full_cube.npz", nsurveys + ) + elif pargs.run == "lmF": # Emax input_file = "Cubes/craco_lm_F_cube.json" From a300538f93e9fc838141f535a39fa1014ec62a9d Mon Sep 17 00:00:00 2001 From: Jay Baptista Date: Sun, 30 Oct 2022 22:31:52 +0000 Subject: [PATCH 065/104] debugging NaN slices in F|H0 --- papers/F/Analysis/CRACO/2d.ipynb | 236 +++ .../OutputMini1.0/nautilus_craco_mini.yaml | 80 + .../CRACO/Cubes/craco_mini_cube_old.json | 80 + papers/F/Analysis/CRACO/Fussing_on_Full.ipynb | 1316 +++++++++++++++++ papers/F/Analysis/CRACO/Untitled.ipynb | 162 ++ papers/F/Analysis/CRACO/marginalize.ipynb | 7 +- papers/F/Analysis/CRACO/test.py | 138 ++ papers/F/Analysis/CRACO/testF.py | 138 ++ papers/F/Figures/py/figs_zdm_F_I.py | 18 +- papers/F/Figures/py/make_figs.py | 9 + 10 files changed, 2171 insertions(+), 13 deletions(-) create mode 100644 papers/F/Analysis/CRACO/2d.ipynb create mode 100644 papers/F/Analysis/CRACO/Cloud/OutputMini1.0/nautilus_craco_mini.yaml create mode 100644 papers/F/Analysis/CRACO/Cubes/craco_mini_cube_old.json create mode 100644 papers/F/Analysis/CRACO/Fussing_on_Full.ipynb create mode 100644 papers/F/Analysis/CRACO/Untitled.ipynb create mode 100644 papers/F/Analysis/CRACO/test.py create mode 100644 papers/F/Analysis/CRACO/testF.py create mode 100644 papers/F/Figures/py/make_figs.py diff --git a/papers/F/Analysis/CRACO/2d.ipynb b/papers/F/Analysis/CRACO/2d.ipynb new file mode 100644 index 00000000..b2f4c187 --- /dev/null +++ b/papers/F/Analysis/CRACO/2d.ipynb @@ -0,0 +1,236 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "caf56b85-94a0-4e98-8c2a-7dba1111aa23", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import zdm.analyze_cube as ac\n", + "cube_dir = \"../CRACO/Cubes/craco_mini_cube.npz\"\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "f5e56bdd-a957-4150-a519-5dbedeeece6b", + "metadata": {}, + "outputs": [], + "source": [ + "cube=np.load(cube_dir)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "8527cf7d-c26f-47c5-a71c-1aee29ca3f6e", + "metadata": {}, + "outputs": [], + "source": [ + "lls = cube[\"ll\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "7514ab70-4500-420e-b578-30cad4d806f9", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jovyan/zdm/zdm/analyze_cube.py:505: RuntimeWarning: All-NaN slice encountered\n", + " themax = np.nanmax(lls)\n", + "/home/jovyan/zdm/zdm/analyze_cube.py:517: RuntimeWarning: All-NaN slice encountered\n", + " wthemax = np.nanmax(wlls)\n" + ] + } + ], + "source": [ + "uvals, ijs, arrays, warrays = ac.get_2D_bayesian_data(cube['ll'])" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "61316a13-7fc2-47ee-85ca-549bba5774f0", + "metadata": {}, + "outputs": [], + "source": [ + "def ij_idx(param_1, param_2):\n", + " idx_1 = np.where(cube[\"params\"] == param_1)[0][0]\n", + " idx_2 = np.where(cube[\"params\"] == param_2)[0][0]\n", + " return np.where((np.array(ijs) == [idx_1, idx_2])[:, 0] & (np.array(ijs) == [idx_1, idx_2])[:, 1])[0][0]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "294f18fb-3a00-40a0-b7dd-cf61500c7729", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(dpi=100)\n", + "\n", + "xx, yy = np.meshgrid(cube[\"H0\"], 10**(cube[\"logF\"]))\n", + "data = np.array(arrays[ij_idx(\"H0\", \"logF\")])\n", + "\n", + "array = data\n", + "# array -= np.max(array)\n", + "# array = 10**array\n", + "# array /= np.sum(array)\n", + "\n", + "f = ax.pcolormesh(xx, yy, array.T)\n", + "ax.set_xlabel(r\"$H_0$\")\n", + "ax.set_ylabel(\"F\")\n", + "plt.colorbar(f, ax=ax, label=r\"$p(H_0 | F)$\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "6e84f3ce-e426-4104-8440-ed40325ad205", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(dpi=100)\n", + "\n", + "xx, yy = np.meshgrid(cube[\"H0\"], (cube[\"logF\"]))\n", + "data = np.array(arrays[ij_idx(\"H0\", \"logF\")])\n", + "\n", + "array = data\n", + "# array -= np.max(array)\n", + "# array = 10**array\n", + "# array /= np.sum(array)\n", + "\n", + "f = ax.pcolormesh(xx, yy, array.T)\n", + "ax.set_xlabel(r\"$H_0$\")\n", + "ax.set_ylabel(r\"$\\log F$\")\n", + "plt.colorbar(f, ax=ax, label=r\"$p(H_0 | \\log F)$\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "1aca9fda-7e1f-4e14-9d25-403147e697c0", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(dpi=100)\n", + "\n", + "xx, yy = np.meshgrid(cube[\"lmean\"], 10**(cube[\"logF\"]))\n", + "data = np.array(arrays[ij_idx(\"lmean\", \"logF\")])\n", + "ax.pcolormesh(xx, yy, data.T)\n", + "ax.set_xlabel(\"lmean\")\n", + "ax.set_ylabel(\"F\")\n", + "plt.colorbar(f, ax=ax, label=r\"$p(\\mathrm{lmean} | F)$\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "da910ce0-deb7-4a39-a273-08022adc146d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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6zRnAOcDHgWWSSj0zayNibSP3HQqc2FhuYtPG+kEGwLANXmm0EX/YZ0PRTegYO3zHHfD1DRtCM0EGJyKukrQ9cCawI3A/MC0inkhDdiRJOErxSyVNAy4APgM8DZwaEVeXVXsSMBL4ccXtvgKc3eB9C+fExszMLCft3FIhIuYB82qcm17l2K3AHgPU97bB3ncocGJjZmaWE+8VVTwnNmZmZjmJEJExUckabwNzYmNmZpaTZja1bHYTTKvOiY2ZmVlOPBRVPCc2ZmZmOfFQVPH8fqCZmZl1DffYmJmZ5cRDUcVzYmNmZpYTD0UVz4mNmZlZTqKJHhsnNvlyYmNmZpaTACLjtg1dssvDkOHExszMLCf9CHkdm0I5sTEzM7O2k7QFMBbYGnguIl7Io96Oed1b0hslLZC0Oi0LJL2hget2lXRtes0aSXdIGl/vOjMzs6xKk4ezll4haRtJn5Z0C7AaWAY8CDwn6QlJF0t672Du0Uk9NlcA44BD08/fBRYAH651gaR3ALcDlwJnkTzEXYF1LW1pj4p+jxQ3autneucPssE4/tD/LLoJHeP64TsU3YQhT9EPm1p7j/4Q8uveVUn6e+BLJMnMtcB5wFPAn4DtgL8A9gd+KekO4JSI+G3W+3REYiNpV5KEZp+IWJweOwFYJGlCRDxS49Jzgesi4oyyY4+3trVmZtarIpqYPNw7/ybcFzgwIu6rcf5O4DJJM4DjgalA5sSmU4aipgCrS0kNQETcQdIDs2+1CyQNAz4EPCrpRknPSlos6fB2NNjMzHqPh6Jqi4i/AZY2ELc+IuZFxCXN3KdTEpuxwLNVjj+bnqtmDLAN8AXgBuBg4BrgJ5Km1rqRpC0lbVsqwKhBtdzMzHqGE5u6/iCppeOmhSY2ks6WFHXK5DS8WmedahyHV3+2n0bEBRFxT0ScB/wcmDFAs2aR9ASVypPZfzIzM+tFpS0VspYeMpyy3EPSryW9Oc8bFD3H5iLgyjoxy4DdgWo/+JuAlTWuW0UyTezBiuMPAfsNcL/ZwJyyz6NwcmNmZtYKuwOvz7PCQhObiFhFkoAMSNIiYLSkvSLizvTY3sBoYGGNujdI+g0woeLUO4EnBmjTemB92b3rNc/MzAzw5OGhoCPm2ETEQyTzZC6WtI+kfYCLgZ+XvxEl6WFJR5Rd+g3gaEknSPpzSSeTvB4+r53tNzOz3pAkNlnn2BTd6rb7uKQ90gX6IOddJYoeisriGGAucFP6+Vrg5IqYCSS9OABExDXpa2Oz0msfAY6KiNtb31wzM+s13t27rtuBr5BM89hIkoecK+k24L+B/4mIQa011zGJTbrU8rF1Yjb77YiIy4DLWtUuMzOzkiB790MvddhExPsBJO0C7AnskX49F3gDsEnSwxGxe7P36JjExszMbKhzj01j0hWFf0vZC0SSdgYmA5MGU7cTGzMzMytcRCwlWcDvR4Opx4mNmZlZXjwWVTgnNmZmZnlpZiXhHhyKaiUnNmZmZjnxOjaNkTSnxqkA1gGPkewc8ELWup3YmJmZ5cSThxs2ieSNqOEkS7EI2AXoAx4GTgK+KWm/iKjcQWBATmwsP9FfdAs6xujH+4puQkf47BuXFd2EjnHD8Fy32+lKir5ko51WCmUfWurNxOanwAvA30bESwDpxtOXkqx1czFwBXABcEiWijti5WEzM7NOUBqKylp60OeAfyglNQDp92cDZ0TEy8BXSda4ycSJjZmZmbXbaGBMleNvArZNv38RGJm1Yic2ZmZmeYkmS+/5KXCZpCMkjZO0U7rX46XAf6QxewGPZq3Yc2zMzMxy4snDDfs0yfyZK0lyEZHsHfV94O/TmIeBv8tasRMbMzOzPPVmD0wmEbEWOEHS3wNvJ0lsfpceL8Xc00zdTmzMzMxy4h6bxkl6A3A8sCtJOvigpMsiYvVg6vUcGzMzs7x4jk1DJE0Gfkcy7LQdsANwGvA7SXsMpm732JiZmeVGacl6Tc+5ALgWOCEiNgFIGgFcAlwIvL/Zip3YmJmZWbtNpiypAYiITZL+CVgymIo9FGVmZpYXD0U16iVgfJXjfwasGUzFTmzMzMzy4sSmUVcBl0o6WtKfpWvZfJRkKOoHg6nYQ1FmZmZ58V5RjTqdJKW7nFdzkY3At4EvDKZiJzZmZmY5aWbvp17cKyoiNgCflTQLeAfJDOrH0j2iBsVDUWZmZnlp41CUpJMkLZW0TtJdkvavEz81jVsn6XFJMyrOv0vS1ZKWSQpJM6vUcXZ6rrysaO4ngIh4OSLui4h780hqwD02ZoUY9dtBrT/VM/59zXZFN8FsSJJ0NMlr0ScBvybZouB6SbtFxPIq8TsD1wEXA8cC7wPmSXouIq5Ow7YGHgd+RPI6di0PAB8s+9zXYJvnNBIHEBGnNRpbyYmNmZlZXto3x+Y04NKIuCT9PFPSIcCJwKwq8TOA5RExM/38ULpI3unA1QAR8RvgNwCSzhvg3psioplemkkNxg1qcK5jEhtJbwTmAoelh64FTomIFwe4ZhvgPOBwYHtgGTA3Ir7dyraamVlvUiQl6zWZ4qWRwJ4kf7+VuwnYt8ZlU9Lz5W4Ejpe0RURszNCEXSQ9DawHFgNfjIjH610UEQdmuEfTOmmOzRXARODQtEwEFtS55oI09liSvSguAP5F0kda1kozM+tdg5tjM0rStmVlyxp32QEYDqysOL4SGFvjmrE14kek9TVqMXAccAhwQlrvQknbZ6ijpTIlNpI+OcCDbhlJu5IkKH8XEYsiYhHJA/0rSRMGuHQK8P2IuCUilkXEd4H/IVnx0MzMLF+loaisJfEksLqsVBtSes3dKj6ryrF68dWO164g4vqIuDqd8Hsz8KH01CcaraPVsg5FXQz8HHgWIO2K2jciluXcrkpTgNURsbh0ICLukLSapNvtkRrX3Q4cJuky4GngAOCdwGdr3ShN3MqTt1GDa7qZmfWMZt5yejV+HK9ddXd9jStWkUzYreydGcPmvTIlK2rEbwKeb7Clm4mIP0q6D9ilXqykpTQ3f+bCiJjbaHDWxKZyhtMo2jOcNZY0marwLLW73QBOJUnGniT5P6+fpNfn9gGumQWc1WQ7zcyslw0usVkTES/VDY/YIOku4CDgmrJTBwE/rXHZIuDDFccOBpZknF/zGmlnwK7AbQ2ET2/yNsuyBBc6eVjS2dRPIt6bfq32q1Kv2+1UYB+SCcdPkOwWOk/SM2kXWjWzgfJX0kaRJEZmZmZDxRxggaQlJEnLp0j2XpoPIGk2sFNEHJfGzwdOTl+5vphkJOR44GOlCtNJybulH0cCO0maCKyNiMfSmPOBnwHLSXp8vgxsC3y/XoMj4tZB/LwNy5rYVOaig93l4iLgyjoxy4DdgTdXOfcmanS7SXod8HXgiIj4RXr43vT/pNOBqolNRKynrPtP6smlrs3MrBmD67Fp/JKIq9IJu2cCOwL3A9Mi4ok0ZEfKNpmMiKWSppG8RPMZkukZp5atYQPwFuDuss+np+VWkqkckAyX/YBkwvFzwB3APmX3LVwzQ1GPSq+8nLYNcLek/vKgiGhoVa2IWEUyVjjwTaVFwGhJe0XEnemxvYHRwMIal22Rlv6K43101ttgZmbWKdq4V1REzAPm1Tg3vcqxW4E9BqhvGZtPOamM+WimRg5A0geAD5D0/Lzm7+WI+GSz9WZNbP622RsNRkQ8JOkG4GJJn04Pfxf4eUS8MnFY0sPArIi4JiJeknQr8A1JfyIZippK8ppa0ysampmZ1dKOdWy6gaSzSHqblgDPkOMe55kSm4ioO4bWQseQLNBXWmDoWuDkipgJJL04JR8lmTPz78B2JMnNl0jHIM3MzHLVpqGoLjADmB4R9dajy6xjVh6OiBdIFtobKEYVn1dQUC+TmZmZ1TSS2lNJBqWpuSaS/iDphSrleUlPSbpVkhMKMzMzq+YS4OOtqLjZHpuvkgzpXA/cSTLZ6L0kqwN/C9gZ+LakERFxcR4NNTMzG+pEE3NsWtKSIW8r4FOSPgjcC7xmLZ0idvfeD/hyRLxmrko6sffgiDhK0r28ukCemZV7qtbioFbu/G8dXXQTOsabNy2uH9TjIja14Sbteyuqw+0O3JN+/xcV5wrZ3fsQ4PNVjv8n8M30++vYfOdRMzOz7uXJww1p5U7fzSY2L5AszXxBxfEPp+cAXs9r97wwMzPrbk5sMpG0G8lCgiPLDkdE/KzZOptNbM4hmUNzIMkcmwD2AqaRvMIFyZ4VbVk+2czMbCjwOjaNkfR2kn2u3k2SQ1TuND682bqbeisqnRA8FfgjcCTw18DLwNSIuDSN+WZEeIDczMx6RzRZes8/A0tJtkt6GXgXyX6OS3h1+4amNL2OTUT8Gvj1YG5uZmZmPWkK8JcR8Vy6LVN/RNwuaRbJYryTmq246cRG0nDgcJLtygN4ELg2IvqardPMzKyjeY5No4YDa9PvV5FswPkIyQ4BEwZTcVOJjaQ/J3nraae0IQLeCfxe0oci4neDaZSZmVkn8hybht1P8sr348Bi4AxJG4BPpcea1uwu13OB3wF/FhF7RMQkklnNS9NzZmZmvae0jk3W0nu+xqs5yJeBtwK3kbyEdOpgKm52KGoqsE+6fxMAEfG8pC/geTdmZtarPBTVkIi4sez7x4HdJG0H/CEiClmgbz0wqsrxbYANzTfHzMysc3koqnnlnSWD0exQ1M+B70raW6/aB5gPXJtHw8zMzKx7Sdpf0r9JWiRpp/TY/5G032DqbTaxOZVkjs0iYF1aFgKPATMH0yAzM7OO5XVsGiLpKOBG4E8kr3ZvmZ4aBXxxMHU3NRQVES8CH0nfjtqV5K2oByPiscE0xszMrKM1MRTVi4kNyYThGRFxuaSPlh1fCJw5mIobTmwkzakTcoCUzOwezHbjZmZmHcuThxs1AfhVleMvAW8YTMVZemwaXQWwN/8vMsug7w+ri25CR9jx+w8U3YSO0dfntVHracv6sU5sGvUM8OfAsorj+zHIdWwaTmxaucW4mZlZN/BbUQ37DvDPkj5Jktq9RdIU4Hzgq4OpuOktFczMzMyaERH/JGk08F/AViTDUuuB8yPiosHU7cTGzMzM2i4iviTpXGA3kre0H4yItXUuq8uJjZmZWV48x6ZhkrYC/gIYQ5LYjC17CanpNfGc2JiZmeXEc2waI+lQYAGwfZXTQbL7d1OaXaCv7SR9SdJCSS9LerHBayTpbElPS/qTpFskvavFTTUzs17mxfkacRHwQ2DHiBhWUZpOaqCDEhtgJPAj4NsZrjkDOA04GXgvsAL4paRq+1yZmZkNjlcebtQYYE5ErMy74o5JbCLirIi4ALivkXglA3UzgXMj4icRcT/wCWBr4OMta6iZmfWs0lBU1tKDfgwc0IqKu3mOzc7AWOCm0oGIWC/pVmBfknfoNyNpS17dswKq72JuZma2OU8ebtTJwI8k7U/SYbGx/GREzG224m5ObMamXyu7uVYCbx3gulnAWS1pkZmZmUEycnIIySaYB/Da9C6AphObQoei0om9UadMHuRtKnNhVTlWbjYwuqyMG+T9zcysR3goqmFfI9nscnREvC0idi4rbx9MxUX32FwEXFknZlmTda9Iv44l2ZOiZAyb9+K8IiLWk6x+CEDpnXozM7O6PBTVqJHAVRHRn3fFhSY2EbEKWNWi6peSJDcHAXcDSBoJTAU+36J7mplZL3Ni06jvA0cDX8+74qJ7bBomaTywHTAeGC5pYnrqsdISzJIeBmZFxDUREZIuBL4o6bfAb4EvAi8DV7S7/WZm1v28QF/DhgNnSDoEuJfNJw+f1mzFHZPYkOz2+Ymyz3enXw8Ebkm/n0AyL6bkn4DXAfOANwKLgYMjYk1LW2pWT/69r12pb/Xqoptglo17bBr1bl79e/wvKs4N6ol0TGITEdOB6XViVPE5gLPTYmZmZkNARBzYqro7JrExMzMb8txjUzgnNmZmZjnxHJvaJM1pNLZX5tiYmZkNbe6xGcikBuN6Y46NmZnZUOcem9paOa+mnBMbMzOzvLjHpnAds7u3mZnZkBdNlh6QrkeXJX6nZu7jxMbMzKwDSTpJ0lJJ6yTdle6UPVD81DRunaTHJc2oOP8uSVdLWpbu1Tgzj/uW+Y2kiyXtNUAbR0s6QdL9wJEN1vsaHooyMzPLidKS9ZrM95GOBi4ETgJ+DXwauF7SbhGxvEr8zsB1wMXAscD7gHmSnouIq9OwrYHHgR8BF+Rx3wq7kuwAcIOkjcAS4GlgHckiursB70qPfy4irq//JDbnHhszM7O8tG8o6jTg0oi4JCIeioiZwO+BE2vEzwCWR8TMNP4S4DLg9FeaHvGbiPhcRFxJ2WbQg7zvKyLihYg4HXhLGv8osAOwSxry78CeEfG+ZpMacI+NmZlZbtrxVlS6ofOewHkVp24C9q1x2ZT0fLkbgeMlbRERG6tck8d9NxMR64CfAD8pzaOJiKcavb4e99iYmZnlZXA9NqMkbVtWtqxxlx1INpFcWXF8JTC2xjVja8SPSOtrRDP3rUrS+yQtBZYDyyWtlPSPkrbNUk81TmzMzMzy1Pww1JPA6rIyq4E7lVOVY/Xiqx2vJ+t9q/kO8ADwXmB34HPAB4C7JDWaaFXloSgzM7OhYRywpuxzrXkuq4A+Nu8lGcPmvSklK2rEbwKeb7B9zdy3lncAR0TEb9PPD0haAPwQmAt8PGN9r3CPjZmZWU5Kc2yyltSaiHiprFRNbCJiA3AXcFDFqYOAhTWatqhK/MHAkkbm1wzivrU8REWCFBEBnAl8OGNdr+EeGzMzs7y0b+XhOcACSUtIkpZPAeOB+QCSZgM7RcRxafx84OR0I8qLSSYTHw98rFRhOjl4t/TjSGAnSROBtRHxWCP3zeB7wHclHVLxmvhokmG4pjmxMTMzy0m79oqKiKskbU/Sw7EjcD8wLSKeSEN2JEk4SvFLJU0jWZ/mMyTrx5xatoYNJK9h3132+fS03Aoc0OB9G3Vh+vVRST8B7iGZmHwsyXybpjmxMTMzy0sb94qKiHnAvBrnplc5diuwxwD1LaOB9QIHum8GY0l2+34PMBGYTrKeTQBfkPQh4F7g3oi4IUvFTmzMzMxy4t29GxMRz5Kso3Nj6ZikrYB3kyQ67wEOI1mp+A1Z6nZiY2Zmlhfv7t20dOG+36SlaX4ryszMzLqGe2zMzMzy4h6bwjmxMTMzy4nn2BTPiY2ZmVle3GNTuI6ZYyPpS5IWSnpZ0osNxG+Rbqh1n6Q/Snpa0uWS3tKG5pqZWQ9SRFPF8tMxiQ3JKog/Ar7dYPzWJO/rn5N+PRJ4J3BtS1pnZmY2uN29LQcdMxQVEWcBSJreYPxqKvazkHQKcKek8RVLOJuZmVkX6JjEJiejSXLjF2sFSNoS2LLs0KgWt8nMzLqEJw8Xr5OGogYlXdHwPOCKiHhpgNBZJBtwlcqTbWiemZl1Aw9FFa7QxEbS2ZKiTpmcw322AK4k+XlPqhM+m6Rnp1TGDfb+ZmbWG0o9NlmL5afooaiLSBKOgSwbzA3SpOaHwM7AX9bprSEi1gPry64fzO3NzKyX+HXvwhWa2ETEKmBVq+ovS2p2AQ6MiOdbdS8zMzPPsSlex8yxkTRe0kRgPDBc0sS0bFMW87CkI9LvRwA/BiYDx6TXjE3LyAJ+BDMz63aeY1O4ooeisvgq8Imyz3enXw8Ebkm/n0AyLwaSuTGHpd/fU1FX+TVmZmbWJTomsYmI6cD0OjEq+34Z4AkyZmbWVh5aKlbHJDZmZmZDXkRSsl5juXFiY2ZmlhNPHi6eExszM7O8+HXvwjmxMTMzy4n6k5L1GstPx7zubWZmZlaPe2zMzMzy4qGowjmxMTMzy4knDxfPiY2ZmVle/Lp34ZzYmJmZ5cQ9NsVzYmNmZpYXz7EpnBMbMzOznLjHpnh+3dvMzMy6hntszMzM8uLJw4VzYmNmZpYTD0UVz4mNmZlZXjx5uHBObMzMzHLiHpviObExMzPLS38kJes1lhsnNmZmZnnxUFTh/Lq3mZmZdQ332JiZmeVENDHHpiUt6V1ObMzMzPLidWwK58TGzMwsJ34rqngdM8dG0pckLZT0sqQXm7j+O5JC0sz8W2dmZsark4ezFstNxyQ2wEjgR8C3s14o6XBgb+DpnNtkZmb2CkU0VSw/HTMUFRFnAUianuU6STsBFwGHAL/Iv2VmZmY2VHRMYtMMScOABcA3IuIByXPPzcyshfrTkvUay00nDUU14/PAJmBuoxdI2lLStqUCjGpZ68zMrKu0cyhK0kmSlkpaJ+kuSfvXiZ+axq2T9LikGVVijpL0oKT16dcjKs6fnc5XLS8rmvoBWqTQxKbGA6osk5use0/gs8D0iEy/NbOA1WXlyWbub2ZmPahNk4clHQ1cCJwLTAJuA66XNL5G/M7AdWncJODrwFxJR5XFTAGuIhnpeE/69YeS9q6o7gFgx7Ly7uw/QesUPRR1EXBlnZhlTda9PzAGWF42BDUc+KakmRHxthrXzQbmlH0ehZMbMzNrRPvWsTkNuDQiLkk/z5R0CHAiyT/QK80AlkfEzPTzQ2nHwenA1aU6gF9GxOz082xJU9PjHyura1NEDKlemnKFJjYRsQpY1aLqFwA3Vxy7MT3+rwO0aT2wvvTZ83LMzKxR7VjHRtJIYE/gvIpTNwH71rhsSnq+3I3A8ZK2iIiNacwFVWJmVhzbRdLTJH9XLga+GBGPZ/ohWqjoHpuGpd1r2wHjgeGSJqanHouItWnMw8CsiLgmIp4Hnq+oYyOwIiIeaV/LzcysZwyux2ZUxT+m16f/2K60A8kIxMqK4yuBsTXuMrZG/Ii0vmcGiCmvczFwHPAo8Gbgy8BCSe9K/94tXMckNsBXgU+Ufb47/XogcEv6/QRgdBvbZGZmlpfKaQ9fAc4eIL4yg1KVY/XiK48PWGdEXF927j5Ji4Dfkfz9PIchoGMSm4iYDkyvEzPguNEA82rMzMwGTf1JyXpNahywpuxUtd4aSKZw9LF578wYNu9xKVlRI34Tr45u1IqpVScR8UdJ9wG71Ippt25/3dvMzKx9SkNRWUtiTUS8VFaqJjYRsQG4Czio4tRBwMIaLVtUJf5gYEk6v2agmFp1ImlLYFeSoawhoWN6bMzMzIa8Zl7fbm4ZmznAAklLSBKST5HMQZ0PIGk2sFNEHJfGzwdOljQHuJhkovDxvPZtp38GfiXp88BPgY8AHwT2KwVIOh/4GbCcpDfny8C2wPeb+ilawImNmZlZTppZcK+ZBfoi4ipJ2wNnkqwlcz8wLSKeSEN2JEl0SvFLJU0jeevpMyR7J54aEVeXxSyU9FHga8A5JHNnjo6IxWW3Hgf8gGTC8XPAHcA+ZfctnBMbMzOzvLRvHRsiYh4wr8a56VWO3QrsUafOHwM/HuD8R7O1sv08x8bMzMy6hntszMzM8hJk39SyuQ4bq8GJjZmZWU7aNcfGanNiY2ZmlpegiTk2LWlJz3JiY2Zmlpc2Th626pzYmJmZ5aWfVzcqyHKN5caJjZmZWU48x6Z4ft3bzMzMuoZ7bMzMzPLiOTaFc2JjZmaWFyc2hXNiY2ZmlhcnNoVzYmNmZpYXvxVVOCc2ZmZmOfFbUcXzW1FmZmbWNdxjY2ZmlhfPsSmcExszM7O89AcoY6LS78QmT05szMzM8uIem8I5sTEzM8tNE4mNt/fOlRMbMzOzvLjHpnAd81aUpC9JWijpZUkvZrhuV0nXSlotaY2kOySNb2FTzcysV/VHc8Vy0zGJDTAS+BHw7UYvkPQO4HbgYeAA4D3AOcC6FrTPzMzMCtYxQ1ERcRaApOkZLjsXuC4izig79nie7TIzM3tF9Ccl6zWWm07qsclE0jDgQ8Cjkm6U9KykxZIOr3PdlpK2LRVgVDvaa2ZmXaA0xyZrsdx0bWIDjAG2Ab4A3AAcDFwD/ETS1AGumwWsLitPtridZmbWLTzHpnCFJjaSzpYUdcrkJqsv/Ww/jYgLIuKeiDgP+DkwY4DrZgOjy8q4Ju9vZma9xj02hSt6js1FwJV1YpY1WfcqYBPwYMXxh4D9al0UEeuB9aXPUtZtWs3MrGcFTbzu3ZKW9KxCE5uIWEWSgLSi7g2SfgNMqDj1TuCJVtzTzMx6nNexKVzRPTYNS9ee2Q4YDwyXNDE99VhErE1jHgZmRcQ16blvAFdJ+hXwX8ChwIdJXv02MzOzLtMxiQ3wVeATZZ/vTr8eCNySfj+BZF4MABFxjaQZJBOC5wKPAEdFxO0tb62ZmfWe/n4g4+vb/X7dO08dk9hExHRgep2YzSbERMRlwGWtaZWZmVkZD0UVrmMSGzMzsyHPiU3hnNiYmZnlpT/I/JqT17HJlRMbMzOznET0Exm3SMgabwPr5pWHzczMrMe4x8bMzCwv0cQWCZ5jkysnNmZmZnmJJubYOLHJlRMbMzOzvPT3gzLOmfEcm1w5sTEzM8uLe2wK58TGzMwsJ9HfT2TssfFbUflyYmNmZpYX99gUzq97m5mZWddwj42ZmVle+gPkHpsiObExMzPLSwSZd/d2YpMrD0WZmZnlJPqjqdIMSSdJWippnaS7JO1fJ35qGrdO0uOSZlSJOUrSg5LWp1+PGOx9282JjZmZWV6iv7mSkaSjgQuBc4FJwG3A9ZLG14jfGbgujZsEfB2YK+mospgpwFXAAuA96dcfStq72fsWQeEusAFJ2hZYfQAfYYS2KLo5ZmbWpE2xkVv4KcDoiHgpz7pf+btCR2T+u2JTbOSWuCZTuyQtBv47Ik4sO/YQ8B8RMatK/D8Ch0XErmXH5gPviYgp6eergG0j4n+XxdwA/CEiPtbMfYvgHhszM7MOImkksCdwU8Wpm4B9a1w2pUr8jcBk6ZVMrFbMvoO4b9t58nCDNrEx89IEZmY2dGxiY+vvEeszDy2VtWuUpPJT6yNifZVLdgCGAysrjq8Exta4zdga8SPS+p4ZIKZUZzP3bTsnNvVtB3A71xXdDjMzy8d2QK5DUcAGYMXtXNfsX/BrgScrjn0FOHuAayr/ua0qx+rFVx5vpM6s920rJzb1vZB+HQesKbIhQ9wokv8o/Zzq87NqjJ9T4/ysGlN6Ti/UC8wqItalE3RH5lhttd4agFVAH5v3koxh896UkhU14jcBz9eJKdXZzH3bzolN49bkPdmsm5R1n/o51eFn1Rg/p8b5WTWmYpgndxGxDljX0psk99kg6S7gIOCaslMHQTI7uopFwIcrjh0MLImIjWUxBwEXVMQsHMR9286JjZmZWeeZAyyQtIQkIfkUMB6YDyBpNrBTRByXxs8HTpY0B7iYZKLw8cDHyur8Z+BXkj5Pkqh8BPggsF+j9x0KnNiYmZl1mIi4StL2wJnAjsD9wLSIeCIN2ZEk4SjFL5U0jaQ35jPA08CpEXF1WcxCSR8FvgacA/wOODoiFme4b+G8jk0dkrYEZgGza8xON/ycsvCzaoyfU+P8rBrj59QbnNiYmZlZ1/ACfWZmZtY1nNiYmZlZ13BiY2ZmZl3DiY2ZmZl1jZ5PbCS9X9LPJD0tKSQd3sA1W0o6V9ITktZL+p2kT7ahuYVp8jkdI+l/JL0s6RlJ/5q+Jti1JM2S9BtJayQ9K+k/JE1o4Lqpku6StE7S45JmtKO9RWnmOUk6UtIvJT0n6SVJiyQd0q42F6XZ36my698naZOke1rYzMIN4r+9nvvzvNv1fGIDvB74H+DkDNf8EPgAyeJGE0gWOHo4/6YNKZmek6T9gMuBS4F3AX8DvBe4pFUNHCKmAt8C9iFZjXMEcJOk19e6IF2G/TrgNmAS8HVgrqSjWt/cwmR+TsD7gV8C00h2GP4v4GeSJrW4rUVr5lkBIGk0yX+H/9nSFg4NzT6nXvzzvKv5de8ykgI4IiL+Y4CYQ4ErgbdHRO77jXSCBp/T6cCJEfGOsmOnAGdExJ+1vpVDg6Q3Ac8CUyPiVzVi/hE4LCJ2LTs2H3hPRExpT0uL1chzqnHdA8BVEfHVljVuiMnyrCRdCfyWZH+fwyNiYutbODQ0+N9ez/953o3cY5PdYcAS4AxJT0l6VNL5kl5XdMOGmIXAOEnTlHgz8NfALwpuV7uNTr8O9IfmFOCmimM3ApMlbdGSVg09jTyn15A0jGRTw177C6mhZyXpb4F3kOwQ3YsaeU7+87wLeUuF7N5Osm/GOuAIYAdgHrAd4HHZVLo09zHAVcBWJL9r1wKnFNqwNpIkkn1Vbo+I+wcIHcvmO+OuJHlmOwDPtKaFQ0OG51Tp/5IMkf6wJQ0bghp9VpJ2Ac4D9o+ITWrx5o9DTYbfKf953oXcY5PdMCCAYyLizoi4DjgNmO4s/1WSdgPmAl8lmQ9xKLAzQ2ijtDa4CNid124yV0vlmLBqHO9GWZ4TAJI+BpxNso/Nsy1q11BU91lJGg5cAZwVEY+2q2FDTKO/U/7zvAu5xya7Z4CnImJ12bGHSP4iGkcynm3Jfiy/johvpJ/vlfRH4DZJX46Ibu+F+BeSbu73R8STdcJXkPTalBsDbAKeb0HzhoyMz6l0zdEkk9L/JiJubmX7hpIMz2oUMBmYJOmi9NiwpAptAg6OiP/X2tYWJ+PvlP8870Luscnu18BbJG1TduydQD/Q0B/MPWJrkmdSri/92rX94ul8oouAI4G/jIilDVy2iOQtjnIHA0siYmPebRwKmnxOpZ6a7wEfj4iemK/VxLN6CXg3MLGszAceSb9fXP2yztbk75T/PO9CPZ/YSNpG0kRJE9NDO6efx6fnZ0u6vOySK0j+Ff2vknaT9H7gG8BlEfGntja+jZp4Tj8DjpR0oqS3S3ofydDUnRHxdHtb31bfAo4FPg6skTQ2La90a1d5VvOBt0qaI2nXdA2N44Hz29ry9sr8nNKk5nKSuTV3lF0zurLyLpPpWUVEf0TcX15I3g5al37+YyE/Res1899eT/553vUioqcLcADJGGtl+V56/nvALRXX/C+S9TReBn4PfBN4XdE/yxB8TqcAD6TP6Wng34Cdiv5ZWvycqj2jAKaXxVR7VlOB/wbWA0uBGUX/LEPtOQG3DPQ72K2l2d+pijrOBu4p+mcZis+pF/887/bidWzMzMysa/T8UJSZmZl1Dyc2ZmZm1jWc2JiZmVnXcGJjZmZmXcOJjZmZmXUNJzZmZmbWNZzYmJmZWddwYmPWoSTdIunCotthZjaUOLExMzOzruHExszMzLqGExuzLiBpmaQvS7pc0lpJT0j6iKQ3Sfppeuw+SZMrrttX0q8k/UnS7yXNlfT6svPHSloiaY2kFZKukDSm7PwBkkLSB9K4lyUtlDShnT+/mVmJExuz7vH3wK+BScAvgAUku2H/G7AH8BhwuSQBSHo3cCPwE2B34GhgP+CisjpHAv8AvAc4HNiZZCPBSueS7Lo9GdgEXJbnD2Zm1ihvgmnWoSTdQrJj80xJy4DbIuL/pOfGAs8A50TEmemxfYBFwI4RsULS5cCfIuLTZXXuB9wKvD4i1lW553uBO4FREbFW0gHAfwEfjIj/TGOmkSRWr6tWh5lZK7nHxqx73Fv2/cr0631VjpWGkvYEpqfDVGslrSXpwRlG0jODpEnpUNYTktYAt6TXjh/g3s9U3MfMrG1GFN0AM8vNxtI3ERHpiNPGsvOl7tlhZV+/A8ytUtfydK7NTWk5FniOJKG5kWSIquq9q9zHzKxtnNiY9a7/Bt4VEY9VO5nOwdkB+EJE/D49NrlarJnZUOF/UZn1rn8Epkj6lqSJknaRdJikf0nPLwc2AKdIerukw0gmEpuZDVlObMx6VETcC0wFdgFuA+4GziGdIxMRzwHTgb8BHgS+AJxeRFvNzBrlt6LMzMysa7jHxszMzLqGExszMzPrGk5szMzMrGs4sTEzM7Ou4cTGzMzMuoYTGzMzM+saTmzMzMysazixMTMzs67hxMbMzMy6hhMbMzMz6xpObMzMzKxrOLExMzOzrvH/AS88OiVLLTkMAAAAAElFTkSuQmCC\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(dpi=100)\n", + "\n", + "xx, yy = np.meshgrid(cube[\"lmean\"], cube[\"logF\"])\n", + "data = np.array(arrays[ij_idx(\"lmean\", \"logF\")])\n", + "ax.pcolormesh(xx, yy, data.T)\n", + "ax.set_xlabel(\"lmean\")\n", + "ax.set_ylabel(\"logF\")\n", + "plt.colorbar(f, ax=ax, label=r\"$p(\\mathrm{lmean} | \\logF)$\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9bf2e3c8-59e4-47c9-b5f5-c9d62fa335d8", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/papers/F/Analysis/CRACO/Cloud/OutputMini1.0/nautilus_craco_mini.yaml b/papers/F/Analysis/CRACO/Cloud/OutputMini1.0/nautilus_craco_mini.yaml new file mode 100644 index 00000000..bbd22a88 --- /dev/null +++ b/papers/F/Analysis/CRACO/Cloud/OutputMini1.0/nautilus_craco_mini.yaml @@ -0,0 +1,80 @@ +# 25 processors on mini for Varying F +# kubectl exec -it test-pod -- /bin/bash +apiVersion: batch/v1 +kind: Job +metadata: + name: xavier-zdm-craco-full-3rd-10 +spec: + backoffLimit: 0 + template: + spec: + affinity: + nodeAffinity: + requiredDuringSchedulingIgnoredDuringExecution: + nodeSelectorTerms: + - matchExpressions: + - key: kubernetes.io/hostname + operator: NotIn + values: + - k8s-chase-ci-01.noc.ucsb.edu + - key: nvidia.com/gpu.product + operator: In + values: + - NVIDIA-GeForce-GTX-1080-Ti + containers: + - name: container + image: localhost:30081/profxj/zdm_docker:latest # UPDATE + imagePullPolicy: Always + resources: + requests: + cpu: "25" + memory: "8Gi" # + ephemeral-storage: 50Gi # + limits: + cpu: "27" + memory: "12Gi" + ephemeral-storage: 100Gi + #nvidia.com/gpu: "1" # See docs to exlude certain types + command: ["/bin/bash", "-c"] + args: + - cd FRB/FRB; + git fetch; + git pull; + python setup.py develop; + cd ../ne2001; + python setup.py develop; + cd ../zdm; + git fetch; + git checkout varying_F; + python setup.py develop; + cd papers/F/Analysis/CRACO/Cloud; + python run_craco_full.py -n 25 -t 25 -b 1; + aws --endpoint http://rook-ceph-rgw-nautiluss3.rook s3 cp Output s3://zdm/Cubes/F/mini/ --recursive --force; + env: + - name: "ENDPOINT_URL" + value: "http://rook-ceph-rgw-nautiluss3.rook" + - name: "S3_ENDPOINT" + value: "rook-ceph-rgw-nautiluss3.rook" + volumeMounts: + - name: prp-s3-credentials + mountPath: "/root/.aws/credentials" + subPath: "credentials" + - name: ephemeral + mountPath: "/tmp" + - name: "dshm" + mountPath: "/dev/shm" + nodeSelector: + nautilus.io/disktype: nvme + restartPolicy: Never + volumes: + # Secrets file for nautilus s3 credentials .aws/credentials and .s3cfg + - name: prp-s3-credentials + secret: + secretName: prp-s3-credentials + # Shared memory (necessary for Python's multiprocessing.shared_memory module to work) + - name: dshm + emptyDir: + medium: Memory + # Ephemeral storage + - name: ephemeral + emptyDir: {} diff --git a/papers/F/Analysis/CRACO/Cubes/craco_mini_cube_old.json b/papers/F/Analysis/CRACO/Cubes/craco_mini_cube_old.json new file mode 100644 index 00000000..cedf3118 --- /dev/null +++ b/papers/F/Analysis/CRACO/Cubes/craco_mini_cube_old.json @@ -0,0 +1,80 @@ +{ + "state": { + "energy": { + "luminosity_function": 2 + }, + "FRBdemo": { + "alpha_method": 1 + }, + "cosmo": { + "fix_Omega_b_h2": true + } + }, + "cube": { + "parameter_order": [ + "lC", + "sfr_n", + "alpha", + "lEmax", + "lmean", + "lsigma", + "F", + "gamma", + "H0" + ] + }, + "lEmax": { + "DC": "energy", + "min": 40.5, + "max": 42.5, + "n": 10 + }, + "H0": { + "DC": "cosmo", + "min": 55.0, + "max": 80.0, + "n": 25 + }, + "alpha": { + "DC": "energy", + "min": 0.2, + "max": 2.0, + "n": 3 + }, + "gamma": { + "DC": "energy", + "min": -0.5, + "max": -1.5, + "n": 5 + }, + "sfr_n": { + "DC": "FRBdemo", + "min": 0.0, + "max": 3.0, + "n": 20 + }, + "lmean": { + "DC": "host", + "min": 1.7, + "max": 2.5, + "n": 5 + }, + "lsigma": { + "DC": "host", + "min": 0.3, + "max": 0.7, + "n": 5 + }, + "F": { + "DC": "IGM", + "min": 0.01, + "max": 0.99, + "n": 20 + }, + "lC": { + "DC": "FRBdemo", + "min": -0.911, + "max": -0.911, + "n": -1 + } +} \ No newline at end of file diff --git a/papers/F/Analysis/CRACO/Fussing_on_Full.ipynb b/papers/F/Analysis/CRACO/Fussing_on_Full.ipynb new file mode 100644 index 00000000..e5845ba6 --- /dev/null +++ b/papers/F/Analysis/CRACO/Fussing_on_Full.ipynb @@ -0,0 +1,1316 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "8e9d01fb-000b-4558-80e8-27688eafa19e", + "metadata": {}, + "source": [ + "# Quick check" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "7a372b56-1bb5-40be-bdf8-4129f926399e", + "metadata": {}, + "outputs": [], + "source": [ + "# imports\n", + "import numpy as np\n", + "import pandas\n", + "\n", + "import seaborn as sns\n", + "\n", + "from IPython.display import display, HTML\n", + "\n", + "from matplotlib import pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "id": "d5e74992-02f4-4cf5-a9af-6672bb8d5b4d", + "metadata": {}, + "source": [ + "# Read one" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "4df320dd-109c-4c74-bc35-760d3d4f07ee", + "metadata": {}, + "outputs": [], + "source": [ + "df_1 = pandas.read_csv(f'Cloud/OutputFull/craco_full1.csv')" + ] + }, + { + "cell_type": "markdown", + "id": "142de13b-5614-457f-b5f1-3cb1151da683", + "metadata": {}, + "source": [ + "## Cut on 55" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "cecb85bb-319b-401a-8fb7-f8a6fe944c00", + "metadata": {}, + "outputs": [], + "source": [ + "idx_55 = np.isclose(df_1.H0, 60.)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "1bc8924e-98b4-4891-8e15-8d52fd12e4db", + "metadata": {}, + "outputs": [], + "source": [ + "df_55 = df_1[idx_55].copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "985660e4-16e2-4cd2-8090-bd0700fe17db", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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41500465.02.0555560.2-1.73.615784-2571.122245-2432.257919-1.899126-136.9652001000.0-2571.122245-2432.257919-1.899126-136.965200-2185.932142-246.325778-2220.175469-212.082451
\n", + "
" + ], + "text/plain": [ + " n H0 lmean lsigma logF lC lls0 P_zDM0 \\\n", + "0 15000 65.0 1.700000 0.2 -1.7 3.604749 -2528.555818 -2389.721168 \n", + "1 15001 65.0 1.788889 0.2 -1.7 3.606677 -2529.497269 -2390.655305 \n", + "2 15002 65.0 1.877778 0.2 -1.7 3.609075 -2536.536738 -2397.687077 \n", + "3 15003 65.0 1.966667 0.2 -1.7 3.612061 -2550.325947 -2411.468768 \n", + "4 15004 65.0 2.055556 0.2 -1.7 3.615784 -2571.122245 -2432.257919 \n", + "\n", + " P_n0 P_s0 N0 lls P_zDM P_n \\\n", + "0 -1.899126 -136.935524 1000.0 -2528.555818 -2389.721168 -1.899126 \n", + "1 -1.899126 -136.942838 1000.0 -2529.497269 -2390.655305 -1.899126 \n", + "2 -1.899126 -136.950535 1000.0 -2536.536738 -2397.687077 -1.899126 \n", + "3 -1.899126 -136.958052 1000.0 -2550.325947 -2411.468768 -1.899126 \n", + "4 -1.899126 -136.965200 1000.0 -2571.122245 -2432.257919 -1.899126 \n", + "\n", + " P_s p_zgDM p_DM p_DMgz p_z \n", + "0 -136.935524 -2141.448538 -248.272630 -2177.664558 -212.056610 \n", + "1 -136.942838 -2143.037752 -247.617553 -2178.593204 -212.062100 \n", + "2 -136.950535 -2150.683298 -247.003779 -2185.618798 -212.068279 \n", + "3 -136.958052 -2164.946656 -246.522112 -2199.393663 -212.075105 \n", + "4 -136.965200 -2185.932142 -246.325778 -2220.175469 -212.082451 " + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_6.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "494bcf9a-bbe4-45c8-b1bc-34e2122dac9e", + "metadata": {}, + "outputs": [], + "source": [ + "idx_677 = np.isclose(df_6.H0, 65.)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "acd86e1b-528d-4462-9eb4-43d79a8167fb", + "metadata": {}, + "outputs": [], + "source": [ + "df_677 = df_6[idx_677].copy()" + ] + }, + { + "cell_type": "markdown", + "id": "3568f064-24f7-4737-8c80-fd0b10274d1e", + "metadata": {}, + "source": [ + "## Plot" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "bc61ed97-e0b2-4ddc-afc5-665bca2869f1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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29971799765.02.3222220.90.03.663436-574.479291-435.744608-1.899126-136.8355561000.0-574.479291-435.744608-1.899126-136.835556-190.436765-245.307844-224.062327-211.682281
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6000 rows × 19 columns

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-1.899126 NaN 1000.0 NaN NaN -1.899126 \n", + "... ... ... ... ... ... ... \n", + "2995 -1.899126 -136.834924 1000.0 -575.011344 -436.277294 -1.899126 \n", + "2996 -1.899126 -136.835244 1000.0 -574.667140 -435.932770 -1.899126 \n", + "2997 -1.899126 -136.835556 1000.0 -574.479291 -435.744608 -1.899126 \n", + "2998 -1.899126 -136.835862 1000.0 -574.453230 -435.718242 -1.899126 \n", + "2999 -1.899126 -136.836162 1000.0 -574.593090 -435.857803 -1.899126 \n", + "\n", + " P_s p_zgDM p_DM p_DMgz p_z \n", + "0 NaN -3518.772936 -248.218411 -3553.960341 -213.031007 \n", + "1 NaN -3531.023333 -247.577938 -3565.561590 -213.039681 \n", + "2 NaN -3549.620662 -246.983542 -3583.554654 -213.049550 \n", + "3 NaN -3574.878377 -246.528137 -3608.345899 -213.060615 \n", + "4 NaN -3606.500502 -246.367388 -3639.795115 -213.072774 \n", + "... ... ... ... ... ... \n", + "2995 -136.834924 -190.715459 -245.561835 -224.616985 -211.660308 \n", + "2996 -136.835244 -190.547491 -245.385280 -224.262260 -211.670511 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = sns.scatterplot(data=df_comb, x='logF', y='lls', hue='H0')\n", + "\n", + "xlim = [-1, 0]\n", + "ax.set_xlim(xlim)\n", + "ax.set_ylim(-600., -550.)\n", + "\n", + "# Max line\n", + "max_LL = df_comb.lls.max()\n", + "ax.plot(xlim, [max_LL]*2, 'g--')" + ] + }, + { + "cell_type": "markdown", + "id": "b5df69af-6901-40c2-beba-0212182bdd16", + "metadata": {}, + "source": [ + "# I am suspecting a slurp bug.." + ] + }, + { + "cell_type": "markdown", + "id": "f5438dc7-61d6-4a67-9aaa-3248fbdc4a5b", + "metadata": {}, + "source": [ + "# I slurped, now am examining the slurped file" + ] + }, + { + "cell_type": "markdown", + "id": "8ef3b730-fea3-40aa-9b7d-34814e9c2024", + "metadata": {}, + "source": [ + "## Load" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "654b497a-e590-4762-a0d2-4ce1c84306dc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['ll',\n", + " 'lC',\n", + " 'params',\n", + " 'pzDM',\n", + " 'pDM',\n", + " 'pDMz',\n", + " 'pz',\n", + " 'H0',\n", + " 'lmean',\n", + " 'lsigma',\n", + " 'logF',\n", + " 'lls0',\n", + " 'P_zDM0',\n", + " 'P_n0',\n", + " 'P_s0',\n", + " 'N0']" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cube = np.load('Cubes/craco_full_cube.npz')\n", + "list(cube.keys())" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "5a1f38c3-2276-4db4-8b85-b562c218f260", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(21, 10, 10, 30)" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "LL = cube['ll']\n", + "LL.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "ffb3969c-843c-4186-8e8b-71132b959441", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "-569.1069" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.nanmax(LL[:,0])" + ] + }, + { + "cell_type": "markdown", + "id": "77e3c617-d266-4a7f-940f-170549619732", + "metadata": {}, + "source": [ + "## Parse" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "b7267261-fb2f-4686-9521-559730c3b3ed", + "metadata": {}, + "outputs": [], + "source": [ + "F = cube['logF']\n", + "H0 = cube['H0']\n", + "#\n", + "dF = F[1]-F[0]\n", + "dH = H0[1] - H0[0]" + ] + }, + { + "cell_type": "markdown", + "id": "59934a22-f603-4c5a-b5b1-86b4cf7b0ac8", + "metadata": {}, + "source": [ + "## Plot" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "adf1fdda-aa1c-4ee2-850d-82f36e5835c3", + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "x and y can be no greater than 2D, but have shapes (21,) and (21, 10, 30)", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "Input \u001b[0;32mIn [35]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m plt\u001b[38;5;241m.\u001b[39mclf()\n\u001b[1;32m 2\u001b[0m ax \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39mgca()\n\u001b[0;32m----> 3\u001b[0m \u001b[43max\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mLL\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4\u001b[0m plt\u001b[38;5;241m.\u001b[39mshow()\n", + "File \u001b[0;32m/opt/conda/lib/python3.9/site-packages/matplotlib/axes/_axes.py:1632\u001b[0m, in \u001b[0;36mAxes.plot\u001b[0;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1390\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1391\u001b[0m \u001b[38;5;124;03mPlot y versus x as lines and/or markers.\u001b[39;00m\n\u001b[1;32m 1392\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1629\u001b[0m \u001b[38;5;124;03m(``'green'``) or hex strings (``'#008000'``).\u001b[39;00m\n\u001b[1;32m 1630\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1631\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m cbook\u001b[38;5;241m.\u001b[39mnormalize_kwargs(kwargs, mlines\u001b[38;5;241m.\u001b[39mLine2D)\n\u001b[0;32m-> 1632\u001b[0m lines \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_lines(\u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39mdata, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)]\n\u001b[1;32m 1633\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m line \u001b[38;5;129;01min\u001b[39;00m lines:\n\u001b[1;32m 1634\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39madd_line(line)\n", + "File \u001b[0;32m/opt/conda/lib/python3.9/site-packages/matplotlib/axes/_base.py:312\u001b[0m, in \u001b[0;36m_process_plot_var_args.__call__\u001b[0;34m(self, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 310\u001b[0m this \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m args[\u001b[38;5;241m0\u001b[39m],\n\u001b[1;32m 311\u001b[0m args \u001b[38;5;241m=\u001b[39m args[\u001b[38;5;241m1\u001b[39m:]\n\u001b[0;32m--> 312\u001b[0m \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_plot_args\u001b[49m\u001b[43m(\u001b[49m\u001b[43mthis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/opt/conda/lib/python3.9/site-packages/matplotlib/axes/_base.py:501\u001b[0m, in \u001b[0;36m_process_plot_var_args._plot_args\u001b[0;34m(self, tup, kwargs, return_kwargs)\u001b[0m\n\u001b[1;32m 498\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx and y must have same first dimension, but \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 499\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhave shapes \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mx\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m and \u001b[39m\u001b[38;5;132;01m{\u001b[39;00my\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 500\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m2\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m y\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m2\u001b[39m:\n\u001b[0;32m--> 501\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx and y can be no greater than 2D, but have \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 502\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mshapes \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mx\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m and \u001b[39m\u001b[38;5;132;01m{\u001b[39;00my\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 503\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m 504\u001b[0m x \u001b[38;5;241m=\u001b[39m x[:, np\u001b[38;5;241m.\u001b[39mnewaxis]\n", + "\u001b[0;31mValueError\u001b[0m: x and y can be no greater than 2D, but have shapes (21,) and (21, 10, 30)" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.clf()\n", + "ax = plt.gca()\n", + "ax.plot(LL[:,0])\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "9b85c517-7fcc-408c-bc68-8f85639b5201", + "metadata": {}, + "source": [ + "## Show it all" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "24b700b6-1433-4a73-bde8-b5f9f9b5fd9c", + "metadata": {}, + "outputs": [], + "source": [ + "nans = np.isnan(LL)\n", + "LL_clean = LL.copy()\n", + "LL_clean[nans] = -9e9\n", + "#\n", + "LL_clean -= LL_clean.max()" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "493e0416-168e-438a-9d29-40b095dbccbf", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'H0 (km/s/Mpc)')" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.clf()\n", + "ax=plt.gca()\n", + "#\n", + "im = plt.imshow(LL_clean.T, origin='lower', vmin=-4., vmax=0., cmap='jet',\n", + " extent=[F.min()-dF/2, F.max()+dF/2, 55.-dH/2, 80+dH/2], aspect='auto')\n", + "# Color bar\n", + "cbar=plt.colorbar(im,fraction=0.046, shrink=1.2,aspect=15,pad=0.05)\n", + "cbar.set_label(r'$\\Delta$ Log10 Likelihood')\n", + "#\n", + "ax.set_xlabel('F')\n", + "ax.set_ylabel('H0 (km/s/Mpc)')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1e57d2a5-f711-4e40-bcf0-4de0576ec60a", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + }, + "vscode": { + "interpreter": { + "hash": "9731a3b7ebb41545a410367c249afbc4842fd5740da82f1bd29e6ac1d7253e6e" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/papers/F/Analysis/CRACO/Untitled.ipynb b/papers/F/Analysis/CRACO/Untitled.ipynb new file mode 100644 index 00000000..c6080848 --- /dev/null +++ b/papers/F/Analysis/CRACO/Untitled.ipynb @@ -0,0 +1,162 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "91a1b4da-2807-4405-a42d-5879947cb622", + "metadata": {}, + "outputs": [], + "source": [ + "# imports\n", + "import numpy as np\n", + "import pandas\n", + "import seaborn as sns\n", + "from IPython.display import display, HTML\n", + "from matplotlib import pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "8cd6f4bd-4ea3-4e54-a560-8352aecee1e8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['ll',\n", + " 'lC',\n", + " 'params',\n", + " 'pzDM',\n", + " 'pDM',\n", + " 'pDMz',\n", + " 'pz',\n", + " 'H0',\n", + " 'lmean',\n", + " 'lsigma',\n", + " 'logF',\n", + " 'lls0',\n", + " 'P_zDM0',\n", + " 'P_n0',\n", + " 'P_s0',\n", + " 'N0']" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cube = np.load('Cubes/craco_full_cube.npz')\n", + "list(cube.keys())" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b001182e-48bb-4a3c-827f-3538c261bb6c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(21, 10, 10, 30)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "LL = cube['ll']\n", + "LL.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "d2d17e43-8ed2-4fdc-b1aa-68247d0a283f", + "metadata": {}, + "outputs": [], + "source": [ + "best_lmean = cube['lmean'][np.isclose(cube['lmean'], 2.16, atol=.05)][0]\n", + "best_lsigma = cube['lsigma'][np.isclose(cube['lsigma'], .51, atol=.05)][0]" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "8cc16752-a1b5-4753-8181-9b9c933a74ae", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['H0', 'lmean', 'lsigma', 'logF'], dtype=' Date: Sun, 30 Oct 2022 22:33:52 +0000 Subject: [PATCH 066/104] debugging NaN slices in F|H0 --- papers/F/Analysis/CRACO/Untitled.ipynb | 162 ------------------------- papers/F/Figures/py/make_figs.py | 9 -- 2 files changed, 171 deletions(-) delete mode 100644 papers/F/Analysis/CRACO/Untitled.ipynb delete mode 100644 papers/F/Figures/py/make_figs.py diff --git a/papers/F/Analysis/CRACO/Untitled.ipynb b/papers/F/Analysis/CRACO/Untitled.ipynb deleted file mode 100644 index c6080848..00000000 --- a/papers/F/Analysis/CRACO/Untitled.ipynb +++ /dev/null @@ -1,162 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "91a1b4da-2807-4405-a42d-5879947cb622", - "metadata": {}, - "outputs": [], - "source": [ - "# imports\n", - "import numpy as np\n", - "import pandas\n", - "import seaborn as sns\n", - "from IPython.display import display, HTML\n", - "from matplotlib import pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "8cd6f4bd-4ea3-4e54-a560-8352aecee1e8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['ll',\n", - " 'lC',\n", - " 'params',\n", - " 'pzDM',\n", - " 'pDM',\n", - " 'pDMz',\n", - " 'pz',\n", - " 'H0',\n", - " 'lmean',\n", - " 'lsigma',\n", - " 'logF',\n", - " 'lls0',\n", - " 'P_zDM0',\n", - " 'P_n0',\n", - " 'P_s0',\n", - " 'N0']" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cube = np.load('Cubes/craco_full_cube.npz')\n", - "list(cube.keys())" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "b001182e-48bb-4a3c-827f-3538c261bb6c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(21, 10, 10, 30)" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "LL = cube['ll']\n", - "LL.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "d2d17e43-8ed2-4fdc-b1aa-68247d0a283f", - "metadata": {}, - "outputs": [], - "source": [ - "best_lmean = cube['lmean'][np.isclose(cube['lmean'], 2.16, atol=.05)][0]\n", - "best_lsigma = cube['lsigma'][np.isclose(cube['lsigma'], .51, atol=.05)][0]" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "8cc16752-a1b5-4753-8181-9b9c933a74ae", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['H0', 'lmean', 'lsigma', 'logF'], dtype=' Date: Sun, 30 Oct 2022 18:35:29 -0400 Subject: [PATCH 067/104] testing cubes and add FRBs to pzdm contours --- papers/F/Analysis/CRACO/py/cube_test.ipynb | 27 ++--- papers/F/Figures/py/figs_zdm_F_I.py | 126 ++++++++++++++++----- 2 files changed, 108 insertions(+), 45 deletions(-) diff --git a/papers/F/Analysis/CRACO/py/cube_test.ipynb b/papers/F/Analysis/CRACO/py/cube_test.ipynb index 77d2c81f..20a59871 100644 --- a/papers/F/Analysis/CRACO/py/cube_test.ipynb +++ b/papers/F/Analysis/CRACO/py/cube_test.ipynb @@ -16,7 +16,7 @@ "metadata": {}, "outputs": [], "source": [ - "cube = np.load(\"../Cubes/craco_H0_F_cube.npz\")" + "cube = np.load(\"../Cubes/craco_H0_logF_cube.npz\")" ] }, { @@ -42,27 +42,27 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ - "F = cube[\"F\"]\n", + "logF = cube[\"logF\"]\n", "H0 = cube[\"H0\"]" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ - "dF = F[1]-F[0]\n", + "dF = logF[1]-logF[0]\n", "dH = H0[1] - H0[0]" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -72,12 +72,12 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -92,23 +92,16 @@ "fig, ax = plt.subplots()\n", "\n", "im=ax.imshow(ll.T,cmap='jet',origin='lower', \n", - " interpolation='None', extent=[F.min()-dF/2, F.max()+dF/2, 55.-dH/2, 80+dH/2], aspect='auto', vmin=-4., vmax=0.)\n", + " interpolation='None', extent=[logF.min()-dF/2, logF.max()+dF/2, 55.-dH/2, 80+dH/2], aspect='auto', vmin=-4., vmax=0.)\n", "# Color bar\n", "cbar=plt.colorbar(im,fraction=0.046, shrink=1.2,aspect=15,pad=0.05)\n", "cbar.set_label(r'$\\Delta$ Log10 Likelihood')\n", "\n", - "ax.set_xlabel('F')\n", + "ax.set_xlabel(f'$\\log F$')\n", "ax.set_ylabel('H0 (km/s/Mpc)')\n", "plt.savefig('fig_H0_vs_F.png', dpi=200)\n", "plt.show()\n" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/papers/F/Figures/py/figs_zdm_F_I.py b/papers/F/Figures/py/figs_zdm_F_I.py index 2f2518b9..fdb1cd5d 100644 --- a/papers/F/Figures/py/figs_zdm_F_I.py +++ b/papers/F/Figures/py/figs_zdm_F_I.py @@ -25,6 +25,7 @@ def fig_craco_varyF_zDM( Aconts=[0.05], fuss_with_ticks: bool = False, suppress_DM_host=False, + iFRB=0, ): """_summary_ @@ -38,7 +39,7 @@ def fig_craco_varyF_zDM( fuss_with_ticks (bool, optional): _description_. Defaults to False. """ # Generate the grid - survey, grid = analy_F_I.craco_mc_survey_grid() + survey, grid = analy_F_I.craco_mc_survey_grid(iFRB=iFRB) fiducial_Emax = grid.state.energy.lEmax fiducial_H0 = grid.state.cosmo.H0 @@ -79,7 +80,7 @@ def fig_craco_varyF_zDM( # Update grid vparams = {} - vparams["F"] = F + vparams["logF"] = F # Sets the log-normal distribution for DM_host to ~0. if suppress_DM_host: @@ -163,6 +164,27 @@ def fig_craco_varyF_zDM( ax.legend(legend_lines, labels, loc="lower right") + # put the FRBs in + + FRBZ = survey.frbs["Z"] + FRBDM = survey.DMEGs + + # Cut down grid + zvals, dmvals, zDMgrid = figures.proc_pgrid( + full_zDMgrid, zvals, (0, zmax), dmvals, (0, DMmax) + ) + ddm = dmvals[1] - dmvals[0] + dz = zvals[1] - zvals[0] + nz, ndm = zDMgrid.shape + + ##### add FRB host galaxies at some DM/redshift ##### + if FRBZ is not None: + iDMs = FRBDM / ddm + iZ = FRBZ / dz + # Restrict to plot range + gd = (FRBDM < DMmax) & (FRBZ < zmax) + ax.plot(iZ[gd], iDMs[gd], "ko", linestyle="", markersize=2.0) + # Fontsize fig_utils.set_fontsize(ax, 16.0) @@ -201,9 +223,10 @@ def fig_varyF( lstyles=["-"], zticks=None, ylim=None, + iFRB=0, ): - survey, grid = analy_F_I.craco_mc_survey_grid() + survey, grid = analy_F_I.craco_mc_survey_grid(iFRB=iFRB) fiducial_F = grid.state.IGM.logF fiducial_Emax = grid.state.energy.lEmax @@ -226,7 +249,7 @@ def fig_varyF( if F is None: F = fiducial_F - vparams["F"] = F + vparams["logF"] = F if other_param == "H0": if other == None: @@ -323,6 +346,23 @@ def fig_varyF( # legend_lines.append(l_mqr[0]) # labels.append("Macquart Relation") + # put down FRBs + + FRBZ = survey.frbs["Z"] + FRBDM = survey.DMEGs + + ddm = dmvals[1] - dmvals[0] + dz = zvals[1] - zvals[0] + nz, ndm = zDMgrid.shape + + ##### add FRB host galaxies at some DM/redshift ##### + if FRBZ is not None: + iDMs = FRBDM / ddm + iZ = FRBZ / dz + # Restrict to plot range + gd = (FRBDM < DMmax) & (FRBZ < zmax) + ax.plot(iZ[gd], iDMs[gd], "ko", linestyle="", markersize=2.0) + ax.legend(legend_lines, labels, loc="lower right") # Fontsize @@ -351,9 +391,9 @@ def fig_craco_fiducial_F( vmnx=(None, None), grid=None, survey=None, - F=0.03, + F=-0.49, H0=None, - iFRB=100, + iFRB=0, suppress_DM_host=False, ): """ @@ -384,10 +424,10 @@ def fig_craco_fiducial_F( fiducial_H0 = grid.state.cosmo.H0 - vparams = {"H0": fiducial_H0, "logF": F} + if H0 is None: + H0 = fiducial_H0 - if H0 is not None: - vparams["H0"] = H0 + vparams = {"H0": H0, "logF": F} if suppress_DM_host: # Sets the log-normal distribution for DM_host to ~0. @@ -447,7 +487,7 @@ def fig_craco_fiducial_F( ax = plt.gca() - ax.set_title(rf"$\log F = {F}$") + ax.set_title(rf"$\log F = {F}$, $H_0$ = {H0}") muDMhost = np.log(10 ** grid.state.host.lmean) sigmaDMhost = np.log(10 ** grid.state.host.lsigma) @@ -533,14 +573,6 @@ def fig_craco_fiducial_F( # suppress_DM_host=False, # ) -fig_craco_fiducial_F( - "fig_craco_logF_-1.51_H0_56.png", - show_Macquart=False, - F=-1.51, - H0=56, - suppress_DM_host=False, -) - #### # fig_craco_varyF_zDM("contours_varyF_H0.pdf", other_param="H0") @@ -605,16 +637,6 @@ def fig_craco_fiducial_F( # DMmax=1800, # ) -# fig_varyF( -# "fig_varyingH0.png", -# other_param="H0", -# F_values=[0.32, 0.32, 0.32], -# other_values=[55, 67.4, 80], -# lcolors=["#f72585", "#f8961e", "#4895ef"], -# lstyles=["-", "-", "-"], -# DMmax=1800, -# ) - ### # fig_varyF( @@ -634,3 +656,51 @@ def fig_craco_fiducial_F( # iFRB = 0 # fig_craco_fiducial_F("fig_craco_fiducial_dmhost_F_0.99_H0_55_i0.png", show_Macquart=False, F=0.99, H0=55., suppress_DM_host=False, iFRB=0) # fig_craco_fiducial_F("fig_craco_fiducial_dmhost_F_0.32_i0.png", show_Macquart=False, F=0.32, suppress_DM_host=False, iFRB=0) + +# diagnostics oct 22 + +logfs = [-1.5, -1.5, -1.5] +h0s = [62.5, 64, 67] + + +for h0, logF in zip(h0s, logfs): + fig_craco_fiducial_F( + f"diagnostic/fig_craco_logF_{logF}_H0_{h0}.png", + show_Macquart=True, + F=logF, + H0=h0, + suppress_DM_host=False, + ) + +# fig_varyF( +# "../diagnostic/varying_a_lot.png", +# other_param="H0", +# F_values=[-1.4, -0.6, -1.4, -0.6], +# other_values=[62.5, 62.5, 70, 70], +# lcolors=["#f72585", "#f8961e", "#4895ef", "#111111"], +# lstyles=["-", "-", "-", "-"], +# DMmax=1800, +# ) + +# fig_varyF( +# "fig_varyF_H0_60.png", +# other_param="H0", +# F_values=[-1.7, -1.2, -0.8], +# other_values=[60.0, 60.0, 60.0], +# lcolors=["#f72585", "#f8961e", "#4895ef"], +# lstyles=["-", "-", "-"], +# DMmax=1800, +# Aconts=[0.01], +# ) + +# fig_varyF( +# "fig_varyF_H0_64.png", +# other_param="H0", +# F_values=[-1.7, -1.2, -0.8], +# other_values=[64.0, 64.0, 64.0], +# lcolors=["#f72585", "#f8961e", "#4895ef"], +# lstyles=["-", "-", "-"], +# DMmax=1800, +# Aconts=[0.01], +# ) + From d0b5a6cc9b660eba34c260b75efccd1d3aef0d7e Mon Sep 17 00:00:00 2001 From: Jay Baptista Date: Sun, 30 Oct 2022 19:32:24 -0400 Subject: [PATCH 068/104] F forecasts on full cube --- zdm/scripts/plot_limits_from_cube.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/zdm/scripts/plot_limits_from_cube.py b/zdm/scripts/plot_limits_from_cube.py index d35d1a6c..137d606c 100644 --- a/zdm/scripts/plot_limits_from_cube.py +++ b/zdm/scripts/plot_limits_from_cube.py @@ -31,7 +31,7 @@ def main(verbose=False): Reiss_sigma = 1.42 ##### loads cube data ##### - cube = "craco_mini_cube.npz" + cube = "../../papers/F/Analysis/CRACO/Cubes/craco_full_cube.npz" data = np.load(cube) if verbose: for thing in data: @@ -54,7 +54,7 @@ def main(verbose=False): tag="", log=False, logspline=False, - kind="linear", + # kind="linear", truth=None, dolevels=True, latexnames=latexnames, From 034235b8078f839b8ce4ec9010b5a1aca1817e62 Mon Sep 17 00:00:00 2001 From: Jay Baptista Date: Fri, 4 Nov 2022 12:47:07 -0400 Subject: [PATCH 069/104] fix Reiss H0 --- papers/F/Figures/py/figs_zdm_F_I.py | 168 +++++---------------------- zdm/scripts/plot_limits_from_cube.py | 2 +- 2 files changed, 31 insertions(+), 139 deletions(-) diff --git a/papers/F/Figures/py/figs_zdm_F_I.py b/papers/F/Figures/py/figs_zdm_F_I.py index fd9441a3..e4d13e65 100644 --- a/papers/F/Figures/py/figs_zdm_F_I.py +++ b/papers/F/Figures/py/figs_zdm_F_I.py @@ -132,12 +132,15 @@ def fig_craco_varyF_zDM( # Label if other_param == "Emax": labels.append( - r"$F = $" + f"{F}, log " + r"$E_{\rm max}$" + f"= {vparams['lEmax']}" + r"$\\log_\{10\} F = $" + + f"{F}, log " + + r"$E_{\rm max}$" + + f"= {vparams['lEmax']}" ) elif other_param == "H0": - labels.append(r"$F = $" + f"{F}, H0 = {vparams['H0']}") + labels.append(r"$\\log_\{10\} F = " + f"{F}, H0 = {vparams['H0']}") elif other_param == "lmean": - labels.append(r"$F = $" + f"{F}, $\mu =$ {vparams['lmean']}") + labels.append(r"$\\log_\{10\} F = " + f"{F}, $\mu =$ {vparams['lmean']}") ###### gets decent axis labels, down to 1 decimal place ####### ax = plt.gca() @@ -318,12 +321,15 @@ def fig_varyF( if other_param == "Emax": labels.append( - r"$F = $" + f"{F}, log " + r"$E_{\rm max}$" + f"= {vparams['lEmax']}" + r"$\\log_\{10\} F = $" + + f"{F}, log " + + r"$E_{\rm max}$" + + f"= {vparams['lEmax']}" ) elif other_param == "H0": - labels.append(r"$F = $" + f"{F}, H0 = {vparams['H0']}") + labels.append(r"$\\log_\{10\} F = $" + f"{F}, H0 = {vparams['H0']}") elif other_param == "lmean": - labels.append(r"$F = $" + f"{F}, $\mu =$ {vparams['lmean']}") + labels.append(r"$\\log_\{10\} F = $" + f"{F}, $\mu =$ {vparams['lmean']}") # # Interpolators # f_DM = interp1d( @@ -565,130 +571,18 @@ def fig_craco_fiducial_F( ### tests -# fig_craco_fiducial_F( -# "fig_craco_logF_-.5_H0_56.png", -# show_Macquart=False, -# F=-0.5, -# H0=56, -# suppress_DM_host=False, -# ) - -# fig_craco_fiducial_F( -# "fig_craco_logF_-1.51_H0_56.png", -# show_Macquart=False, -# F=-1.51, -# H0=56, -# suppress_DM_host=False, -# ) - -#### - -# fig_craco_varyF_zDM("contours_varyF_H0.pdf", other_param="H0") -# fig_craco_varyF_zDM( -# "contours_varyF_H0_dmhost_suppressed.pdf", other_param="H0", suppress_DM_host=True -# ) - -# fig_craco_fiducial_F( -# "fig_craco_F_0.32_dmhost_suppressed.png", -# show_Macquart=True, -# F=0.32, -# suppress_DM_host=True, -# ) -# fig_craco_fiducial_F( -# "fig_craco_F_0.01_dmhost_suppressed.png", -# show_Macquart=True, -# F=0.01, -# suppress_DM_host=True, -# ) -# fig_craco_fiducial_F( -# "fig_craco_F_0.9_dmhost_suppressed.png", -# show_Macquart=True, -# F=0.9, -# suppress_DM_host=True, -# ) - -# fig_craco_fiducial_F( -# "fig_craco_F_0.32.png", show_Macquart=False, F=0.32, suppress_DM_host=False -# ) - -# fig_craco_fiducial_F( -# "fig_craco_F_0.82_H0_55.png", -# show_Macquart=False, -# F=0.82, -# H0=55.0, -# suppress_DM_host=False, -# ) -# fig_craco_fiducial_F( -# "fig_craco_F_0.01.png", show_Macquart=True, F=0.01, suppress_DM_host=False -# ) -# fig_craco_fiducial_F( -# "fig_craco_F_0.9.png", show_Macquart=True, F=0.9, suppress_DM_host=False -# ) - -# fig_varyF( -# "fig_lmean_degeneracy_varyF.png", -# other_param="lmean", -# F_values=[0.01, 0.9], -# other_values=[None, None], -# lcolors=["r", "b"], -# lstyles=["-", "-"], -# DMmax=1800, -# ) - -# fig_varyF( -# "fig_lmean_degeneracy_varylm.png", -# other_param="lmean", -# F_values=[None, None], -# other_values=[2.5, 1.5], -# lcolors=["#e07a5f", "#81b29a"], -# lstyles=["-", "-"], -# DMmax=1800, -# ) - -### - -# fig_varyF( -# "test.png", -# other_param="lmean", -# F_values=[None, None], -# other_values=[1.0, 3.0], -# lcolors=["#e07a5f", "#81b29a"], -# lstyles=["-", "-"], -# DMmax=1800, -# ) - -# Fussing on the square -# fig_craco_fiducial_F("fig_craco_fiducial_dmhost_F_0.32.png", show_Macquart=False, F=0.32, suppress_DM_host=False) -# fig_craco_fiducial_F("fig_craco_fiducial_dmhost_F_0.82_H0_55.png", show_Macquart=False, F=0.82, H0=55., suppress_DM_host=False) - -# iFRB = 0 -# fig_craco_fiducial_F("fig_craco_fiducial_dmhost_F_0.99_H0_55_i0.png", show_Macquart=False, F=0.99, H0=55., suppress_DM_host=False, iFRB=0) -# fig_craco_fiducial_F("fig_craco_fiducial_dmhost_F_0.32_i0.png", show_Macquart=False, F=0.32, suppress_DM_host=False, iFRB=0) +# logfs = [-1.5, -1.5, -1.5] +# h0s = [62.5, 64, 67] -# diagnostics oct 22 -logfs = [-1.5, -1.5, -1.5] -h0s = [62.5, 64, 67] - - -for h0, logF in zip(h0s, logfs): - fig_craco_fiducial_F( - f"diagnostic/fig_craco_logF_{logF}_H0_{h0}.png", - show_Macquart=True, - F=logF, - H0=h0, - suppress_DM_host=False, - ) - -# fig_varyF( -# "../diagnostic/varying_a_lot.png", -# other_param="H0", -# F_values=[-1.4, -0.6, -1.4, -0.6], -# other_values=[62.5, 62.5, 70, 70], -# lcolors=["#f72585", "#f8961e", "#4895ef", "#111111"], -# lstyles=["-", "-", "-", "-"], -# DMmax=1800, -# ) +# for h0, logF in zip(h0s, logfs): +# fig_craco_fiducial_F( +# f"diagnostic/fig_craco_logF_{logF}_H0_{h0}.png", +# show_Macquart=True, +# F=logF, +# H0=h0, +# suppress_DM_host=False, +# ) # fig_varyF( # "fig_varyF_H0_60.png", @@ -701,14 +595,12 @@ def fig_craco_fiducial_F( # Aconts=[0.01], # ) -# fig_varyF( -# "fig_varyF_H0_64.png", -# other_param="H0", -# F_values=[-1.7, -1.2, -0.8], -# other_values=[64.0, 64.0, 64.0], -# lcolors=["#f72585", "#f8961e", "#4895ef"], -# lstyles=["-", "-", "-"], -# DMmax=1800, -# Aconts=[0.01], -# ) +fig_craco_fiducial_F( + f"figs/fiducial.png", + show_Macquart=True, + F=np.round(np.log10(0.32), 3), + H0=None, + suppress_DM_host=False, + iFRB=100, +) diff --git a/zdm/scripts/plot_limits_from_cube.py b/zdm/scripts/plot_limits_from_cube.py index 137d606c..885edc11 100644 --- a/zdm/scripts/plot_limits_from_cube.py +++ b/zdm/scripts/plot_limits_from_cube.py @@ -27,7 +27,7 @@ def main(verbose=False): ######### sets the values of H0 for priors ##### Planck_H0 = 67.4 Planck_sigma = 0.5 - Reiss_H0 = 74.03 + Reiss_H0 = 73.04 Reiss_sigma = 1.42 ##### loads cube data ##### From 4f40fe60feb37ca9d20e525231130b2a31de20ba Mon Sep 17 00:00:00 2001 From: Jay Baptista Date: Mon, 14 Nov 2022 15:09:05 -0500 Subject: [PATCH 070/104] speedtest --- papers/F/speedtest.py | 26 ++++++++++++++++++++++++++ 1 file changed, 26 insertions(+) create mode 100644 papers/F/speedtest.py diff --git a/papers/F/speedtest.py b/papers/F/speedtest.py new file mode 100644 index 00000000..33530529 --- /dev/null +++ b/papers/F/speedtest.py @@ -0,0 +1,26 @@ +from zdm.craco import loading +import time +import numpy as np +import os +from pkg_resources import resource_filename + +i = 30 +res_list = [] + +for i in range(i): + st = time.process_time() + + isurvey, igrid = loading.survey_and_grid( + survey_name="CRACO_std_May2022", + NFRB=100, + lum_func=3, + # sdir=os.path.join(resource_filename("zdm", "data"), "Surveys/James2022a"), + ) + + et = time.process_time() + res = et - st + res_list.append(res) + +avg_res = np.mean(res_list) + +print("CPU Execution time average:", avg_res, "seconds") From 9c4bc6fcfb97c6dea9e1414cc1e41fce2665e8d2 Mon Sep 17 00:00:00 2001 From: Jay Baptista Date: Tue, 15 Nov 2022 22:52:40 -0500 Subject: [PATCH 071/104] prepare real cube --- .../F/Analysis/Real/Cloud/run_craco_real.py | 129 ++++++++++++++++++ .../Analysis/Real/Cubes/craco_real_cube.json | 80 +++++++++++ papers/F/Analysis/Real/py/build_read_cube.py | 70 ++++++++++ zdm/real_loading.py | 6 +- 4 files changed, 282 insertions(+), 3 deletions(-) create mode 100644 papers/F/Analysis/Real/Cloud/run_craco_real.py create mode 100644 papers/F/Analysis/Real/Cubes/craco_real_cube.json create mode 100644 papers/F/Analysis/Real/py/build_read_cube.py diff --git a/papers/F/Analysis/Real/Cloud/run_craco_real.py b/papers/F/Analysis/Real/Cloud/run_craco_real.py new file mode 100644 index 00000000..0b3eb212 --- /dev/null +++ b/papers/F/Analysis/Real/Cloud/run_craco_real.py @@ -0,0 +1,129 @@ +""" Run a Nautilus test """ + +# It should be possible to remove all the matplotlib calls from this +# but in the current implementation it is not removed. +import argparse +import numpy as np +import os, sys +from pkg_resources import resource_filename + +from concurrent.futures import ProcessPoolExecutor +import subprocess + +from zdm import iteration as it +from zdm import io + +from IPython import embed + + +def main( + pargs, + pfile: str, + oproot: str, + NFRB: int = None, + iFRB: int = 0, + outdir: str = "Output", +): + + # Generate the folder? + if not os.path.isdir(outdir): + os.mkdir(outdir) + + ############## Load up ############## + input_dict = io.process_jfile(pfile) + + # Deconstruct the input_dict + state_dict, cube_dict, vparam_dict = it.parse_input_dict(input_dict) + + npoints = np.array([item["n"] for key, item in vparam_dict.items()]) + ntotal = int(np.prod(np.abs(npoints))) + + # Total number of CPUs to be running on this Cube + total_ncpu = pargs.ncpu if pargs.total_ncpu is None else pargs.total_ncpu + batch = 1 if pargs.batch is None else pargs.batch + + nper_cpu = ntotal // total_ncpu + if int(ntotal / total_ncpu) != nper_cpu: + raise IOError(f"Ncpu={total_ncpu} must divide evenly into ntotal={ntotal}") + + survey_file = os.path.join( + resource_filename("zdm", "craco"), "MC_F", "Surveys", "F_0.32_survey" + ) + commands = [] + for kk in range(pargs.ncpu): + line = [] + # Which CPU is running out of the total? + iCPU = (batch - 1) * pargs.ncpu + kk + outfile = os.path.join(outdir, oproot.replace(".csv", f"{iCPU+1}.csv")) + # Command + line = [ + "../py/build_real_cube.py", + "-n", + f"{iCPU+1}", + "-m", + f"{nper_cpu}", + "-o", + f"{outfile}", + "--clobber", + "-p", + f"{pfile}", + ] + # NFRB? + if NFRB is not None: + line += [f"--NFRB", f"{NFRB}"] + # iFRB? + if iFRB > 0: + line += [f"--iFRB", f"{iFRB}"] + # Finish + # line += ' & \n' + commands.append(line) + + # Launch em! + processes = [] + + for command in commands: + # Popen + print(f"Running this command: {' '.join(command)}") + pw = subprocess.Popen(command) + processes.append(pw) + + # Wait on em! + for pw in processes: + pw.wait() + + print("All done!") + + +def parse_option(): + # test for command-line arguments here + parser = argparse.ArgumentParser() + parser.add_argument( + "-n", + "--ncpu", + type=int, + required=True, + help="Number of CPUs to run on (might be split in batches)", + ) + parser.add_argument( + "-t", + "--total_ncpu", + type=int, + required=False, + help="Total number of CPUs to run on (might be split in batches)", + ) + parser.add_argument( + "-b", "--batch", type=int, default=1, required=False, help="Batch number" + ) + # parser.add_argument('--NFRB',type=int,required=False,help="Number of FRBs to analzye") + # parser.add_argument('--iFRB',type=int,default=0,help="Initial FRB to run from") + args = parser.parse_args() + + return args + + +if __name__ == "__main__": + # get the argument of training. + pfile = "../Cubes/craco_full_cube.json" + oproot = "craco_full.csv" + pargs = parse_option() + main(pargs, pfile, oproot, NFRB=100, iFRB=100) diff --git a/papers/F/Analysis/Real/Cubes/craco_real_cube.json b/papers/F/Analysis/Real/Cubes/craco_real_cube.json new file mode 100644 index 00000000..059e43a9 --- /dev/null +++ b/papers/F/Analysis/Real/Cubes/craco_real_cube.json @@ -0,0 +1,80 @@ +{ + "state": { + "energy": { + "luminosity_function": 2 + }, + "FRBdemo": { + "alpha_method": 1 + }, + "cosmo": { + "fix_Omega_b_h2": true + } + }, + "cube": { + "parameter_order": [ + "lC", + "sfr_n", + "lmean", + "lsigma", + "F", + "alpha", + "lEmax", + "gamma", + "H0" + ] + }, + "lEmax": { + "DC": "energy", + "min": 40.5, + "max": 42.5, + "n": 10 + }, + "H0": { + "DC": "cosmo", + "min": 55.0, + "max": 80.0, + "n": 25 + }, + "alpha": { + "DC": "energy", + "min": 0.2, + "max": 2.0, + "n": 3 + }, + "gamma": { + "DC": "energy", + "min": -0.5, + "max": -1.5, + "n": 5 + }, + "sfr_n": { + "DC": "FRBdemo", + "min": 0.0, + "max": 3.0, + "n": 20 + }, + "lmean": { + "DC": "host", + "min": 1.7, + "max": 2.5, + "n": 5 + }, + "lsigma": { + "DC": "host", + "min": 0.3, + "max": 0.7, + "n": 5 + }, + "F": { + "DC": "IGM", + "min": 0.01, + "max": 0.99, + "n": 20 + }, + "lC": { + "DC": "FRBdemo", + "min": -0.911, + "max": -0.911, + "n": -1 + } +} \ No newline at end of file diff --git a/papers/F/Analysis/Real/py/build_read_cube.py b/papers/F/Analysis/Real/py/build_read_cube.py new file mode 100644 index 00000000..6580dce2 --- /dev/null +++ b/papers/F/Analysis/Real/py/build_read_cube.py @@ -0,0 +1,70 @@ +""" Build a log-likelihood cube for zdm for Real FRBs! +""" + +# It should be possible to remove all the matplotlib calls from this +# but in the current implementation it is not removed. +import argparse +import numpy as np +import os + +from zdm import iteration as it +from zdm import io +from zdm import real_loading + +from IPython import embed + +def main(pargs): + + + # Clobber? + if pargs.clobber and os.path.isfile(pargs.opfile): + os.remove(pargs.opfile) + + ############## Load up ############## + input_dict=io.process_jfile(pargs.pfile) + + # Deconstruct the input_dict + state_dict, cube_dict, vparam_dict = it.parse_input_dict(input_dict) + + # State + state = real_loading.set_state() + state.update_param_dict(state_dict) + + ############## Initialise ############## + surveys, grids = real_loading.surveys_and_grids(init_state=state) + + # Write state to disk + state_file = pargs.pfile.replace('cube.json', 'state.json') + state.write(state_file) + + # Set what portion of the Cube we are generating + run=pargs.number + howmany=pargs.howmany + opfile=pargs.opfile + + # checks to see if the file is already there, and how many iterations have been performed + starti=it.check_cube_opfile(run, howmany, opfile) + print("starti is ",starti) + if starti==howmany: + print("Done everything!") + pass + # + it.cube_likelihoods(grids, surveys, vparam_dict, cube_dict, + run, howmany, opfile, starti=starti) + + +def parse_args(options=None): + # test for command-line arguments here + parser = argparse.ArgumentParser() + parser.add_argument('-n','--number',type=int,required=True,help="nth iteration, beginning at 0") + parser.add_argument('-m','--howmany',type=int,required=True,help="number m to iterate at once") + parser.add_argument('-p','--pfile',type=str, required=True,help="File defining parameter ranges") + parser.add_argument('-o','--opfile',type=str,required=True,help="Output file for the data") + parser.add_argument('--clobber', default=False, action='store_true', + help="Clobber output file?") + args = parser.parse_args() + return args + +if __name__ == "__main__": + pargs = parse_args() + main(pargs) \ No newline at end of file diff --git a/zdm/real_loading.py b/zdm/real_loading.py index d752a7d3..6454c9d8 100644 --- a/zdm/real_loading.py +++ b/zdm/real_loading.py @@ -119,9 +119,9 @@ def surveys_and_grids(init_state=None, alpha_method=1, add_20220610A=False): ############## Initialise surveys ############## survey_names = ['CRAFT/FE', - 'private_CRAFT_ICS_1632', - 'private_CRAFT_ICS_892', - 'private_CRAFT_ICS', + 'CRAFT_ICS_1632', + 'CRAFT_ICS_892', + 'CRAFT_ICS', 'PKS/Mb'] if add_20220610A: survey_names[3] = 'CRAFT_ICS_w_220610' From 4b91629e00a7d4cc031ce0c46684ae2231c34af7 Mon Sep 17 00:00:00 2001 From: Jay Baptista Date: Wed, 16 Nov 2022 11:06:55 -0500 Subject: [PATCH 072/104] fix real cube run yaml --- .../Real/Cloud/nautilus_real_cube.yaml | 81 +++++++++++++++++++ .../F/Analysis/Real/Cloud/run_craco_real.py | 5 +- 2 files changed, 82 insertions(+), 4 deletions(-) create mode 100644 papers/F/Analysis/Real/Cloud/nautilus_real_cube.yaml diff --git a/papers/F/Analysis/Real/Cloud/nautilus_real_cube.yaml b/papers/F/Analysis/Real/Cloud/nautilus_real_cube.yaml new file mode 100644 index 00000000..ef381b99 --- /dev/null +++ b/papers/F/Analysis/Real/Cloud/nautilus_real_cube.yaml @@ -0,0 +1,81 @@ +# 21 processors on full for Varying F +# kubectl exec -it test-pod -- /bin/bash +apiVersion: batch/v1 +kind: Job +metadata: + name: x-zdm-craco-full-logf +spec: + backoffLimit: 0 + template: + spec: + affinity: + nodeAffinity: + requiredDuringSchedulingIgnoredDuringExecution: + nodeSelectorTerms: + - matchExpressions: + - key: kubernetes.io/hostname + operator: NotIn + values: + - k8s-chase-ci-01.noc.ucsb.edu + - key: nvidia.com/gpu.product + operator: In + values: + - NVIDIA-GeForce-GTX-1080-Ti + containers: + - name: container + image: localhost:30081/profxj/zdm_docker:latest # UPDATE + imagePullPolicy: Always + resources: + requests: + cpu: "21" + memory: "8Gi" # + ephemeral-storage: 50Gi # + limits: + cpu: "23" + memory: "12Gi" + ephemeral-storage: 100Gi + #nvidia.com/gpu: "1" # See docs to exlude certain types + command: ["/bin/bash", "-c"] + args: + - cd FRB; + git fetch; + git pull; + python setup.py develop; + cd ../ne2001; + python setup.py develop; + cd ../zdm; + git fetch; + git checkout varying_F; + python setup.py develop; + cd papers/F/Analysis/Real/Cloud; + mkdir Output; + python run_craco_real.py -n 21 -t 21 -b 1; + aws --endpoint http://rook-ceph-rgw-nautiluss3.rook s3 cp Output s3://zdm/Cubes/F/real/ --recursive --force; + env: + - name: "ENDPOINT_URL" + value: "http://rook-ceph-rgw-nautiluss3.rook" + - name: "S3_ENDPOINT" + value: "rook-ceph-rgw-nautiluss3.rook" + volumeMounts: + - name: prp-s3-credentials + mountPath: "/root/.aws/credentials" + subPath: "credentials" + - name: ephemeral + mountPath: "/tmp" + - name: "dshm" + mountPath: "/dev/shm" + nodeSelector: + nautilus.io/disktype: nvme + restartPolicy: Never + volumes: + # Secrets file for nautilus s3 credentials .aws/credentials and .s3cfg + - name: prp-s3-credentials + secret: + secretName: prp-s3-credentials + # Shared memory (necessary for Python's multiprocessing.shared_memory module to work) + - name: dshm + emptyDir: + medium: Memory + # Ephemeral storage + - name: ephemeral + emptyDir: {} diff --git a/papers/F/Analysis/Real/Cloud/run_craco_real.py b/papers/F/Analysis/Real/Cloud/run_craco_real.py index 0b3eb212..169380e9 100644 --- a/papers/F/Analysis/Real/Cloud/run_craco_real.py +++ b/papers/F/Analysis/Real/Cloud/run_craco_real.py @@ -46,9 +46,6 @@ def main( if int(ntotal / total_ncpu) != nper_cpu: raise IOError(f"Ncpu={total_ncpu} must divide evenly into ntotal={ntotal}") - survey_file = os.path.join( - resource_filename("zdm", "craco"), "MC_F", "Surveys", "F_0.32_survey" - ) commands = [] for kk in range(pargs.ncpu): line = [] @@ -123,7 +120,7 @@ def parse_option(): if __name__ == "__main__": # get the argument of training. - pfile = "../Cubes/craco_full_cube.json" + pfile = "../Cubes/craco_real_cube.json" oproot = "craco_full.csv" pargs = parse_option() main(pargs, pfile, oproot, NFRB=100, iFRB=100) From 6f7b7df1a501bb57304350504122154178f68404 Mon Sep 17 00:00:00 2001 From: Jay Baptista Date: Wed, 16 Nov 2022 11:12:23 -0500 Subject: [PATCH 073/104] fix typo --- papers/F/Analysis/Real/Cloud/nautilus_real_cube.yaml | 2 +- .../Analysis/Real/py/{build_read_cube.py => build_real_cube.py} | 0 2 files changed, 1 insertion(+), 1 deletion(-) rename papers/F/Analysis/Real/py/{build_read_cube.py => build_real_cube.py} (100%) diff --git a/papers/F/Analysis/Real/Cloud/nautilus_real_cube.yaml b/papers/F/Analysis/Real/Cloud/nautilus_real_cube.yaml index ef381b99..2c1c8f35 100644 --- a/papers/F/Analysis/Real/Cloud/nautilus_real_cube.yaml +++ b/papers/F/Analysis/Real/Cloud/nautilus_real_cube.yaml @@ -3,7 +3,7 @@ apiVersion: batch/v1 kind: Job metadata: - name: x-zdm-craco-full-logf + name: jb-zdm-craco-real-logf spec: backoffLimit: 0 template: diff --git a/papers/F/Analysis/Real/py/build_read_cube.py b/papers/F/Analysis/Real/py/build_real_cube.py similarity index 100% rename from papers/F/Analysis/Real/py/build_read_cube.py rename to papers/F/Analysis/Real/py/build_real_cube.py From fedde12a7ad7cafdbdca86129a4cb02f5e01a8be Mon Sep 17 00:00:00 2001 From: Jay Baptista Date: Wed, 16 Nov 2022 11:19:25 -0500 Subject: [PATCH 074/104] fix yet another typo --- papers/F/Analysis/Real/{py => Cloud}/build_real_cube.py | 0 papers/F/Analysis/Real/Cloud/run_craco_real.py | 1 + 2 files changed, 1 insertion(+) rename papers/F/Analysis/Real/{py => Cloud}/build_real_cube.py (100%) diff --git a/papers/F/Analysis/Real/py/build_real_cube.py b/papers/F/Analysis/Real/Cloud/build_real_cube.py similarity index 100% rename from papers/F/Analysis/Real/py/build_real_cube.py rename to papers/F/Analysis/Real/Cloud/build_real_cube.py diff --git a/papers/F/Analysis/Real/Cloud/run_craco_real.py b/papers/F/Analysis/Real/Cloud/run_craco_real.py index 169380e9..75fa580b 100644 --- a/papers/F/Analysis/Real/Cloud/run_craco_real.py +++ b/papers/F/Analysis/Real/Cloud/run_craco_real.py @@ -54,6 +54,7 @@ def main( outfile = os.path.join(outdir, oproot.replace(".csv", f"{iCPU+1}.csv")) # Command line = [ + 'python', "../py/build_real_cube.py", "-n", f"{iCPU+1}", From 75ac9594648ad140d8c228df06141e7690de84ec Mon Sep 17 00:00:00 2001 From: Jay Baptista Date: Wed, 16 Nov 2022 11:34:50 -0500 Subject: [PATCH 075/104] fix location of real cube --- papers/F/Analysis/Real/Cloud/nautilus_real_cube.yaml | 2 +- papers/F/Analysis/Real/{Cloud => py}/build_real_cube.py | 0 2 files changed, 1 insertion(+), 1 deletion(-) rename papers/F/Analysis/Real/{Cloud => py}/build_real_cube.py (100%) diff --git a/papers/F/Analysis/Real/Cloud/nautilus_real_cube.yaml b/papers/F/Analysis/Real/Cloud/nautilus_real_cube.yaml index 2c1c8f35..8338c817 100644 --- a/papers/F/Analysis/Real/Cloud/nautilus_real_cube.yaml +++ b/papers/F/Analysis/Real/Cloud/nautilus_real_cube.yaml @@ -50,7 +50,7 @@ spec: cd papers/F/Analysis/Real/Cloud; mkdir Output; python run_craco_real.py -n 21 -t 21 -b 1; - aws --endpoint http://rook-ceph-rgw-nautiluss3.rook s3 cp Output s3://zdm/Cubes/F/real/ --recursive --force; + aws --endpoint http://rook-ceph-rgw-nautiluss3.rook s3 cp Output s3://zdm/Cubes/F/full/ --recursive --force; env: - name: "ENDPOINT_URL" value: "http://rook-ceph-rgw-nautiluss3.rook" diff --git a/papers/F/Analysis/Real/Cloud/build_real_cube.py b/papers/F/Analysis/Real/py/build_real_cube.py similarity index 100% rename from papers/F/Analysis/Real/Cloud/build_real_cube.py rename to papers/F/Analysis/Real/py/build_real_cube.py From 92da247c840c516fd5abd79e07aec48654f0883d Mon Sep 17 00:00:00 2001 From: Jay Baptista Date: Wed, 16 Nov 2022 14:20:24 -0500 Subject: [PATCH 076/104] fix arg parser --- papers/F/Analysis/Real/Cloud/run_craco_real.py | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/papers/F/Analysis/Real/Cloud/run_craco_real.py b/papers/F/Analysis/Real/Cloud/run_craco_real.py index 75fa580b..d92f4335 100644 --- a/papers/F/Analysis/Real/Cloud/run_craco_real.py +++ b/papers/F/Analysis/Real/Cloud/run_craco_real.py @@ -54,7 +54,7 @@ def main( outfile = os.path.join(outdir, oproot.replace(".csv", f"{iCPU+1}.csv")) # Command line = [ - 'python', + "python", "../py/build_real_cube.py", "-n", f"{iCPU+1}", @@ -112,8 +112,10 @@ def parse_option(): parser.add_argument( "-b", "--batch", type=int, default=1, required=False, help="Batch number" ) - # parser.add_argument('--NFRB',type=int,required=False,help="Number of FRBs to analzye") - # parser.add_argument('--iFRB',type=int,default=0,help="Initial FRB to run from") + parser.add_argument( + "--NFRB", type=int, required=False, help="Number of FRBs to analzye" + ) + parser.add_argument("--iFRB", type=int, default=0, help="Initial FRB to run from") args = parser.parse_args() return args @@ -122,6 +124,6 @@ def parse_option(): if __name__ == "__main__": # get the argument of training. pfile = "../Cubes/craco_real_cube.json" - oproot = "craco_full.csv" + oproot = "craco_real.csv" pargs = parse_option() main(pargs, pfile, oproot, NFRB=100, iFRB=100) From b591947763c21dfaf4f5fff9d38a6993942f13c0 Mon Sep 17 00:00:00 2001 From: Jay Baptista Date: Wed, 16 Nov 2022 14:27:06 -0500 Subject: [PATCH 077/104] fixing the cube run script --- papers/F/Analysis/Real/Cloud/run_craco_real.py | 8 +------- 1 file changed, 1 insertion(+), 7 deletions(-) diff --git a/papers/F/Analysis/Real/Cloud/run_craco_real.py b/papers/F/Analysis/Real/Cloud/run_craco_real.py index d92f4335..b61bb237 100644 --- a/papers/F/Analysis/Real/Cloud/run_craco_real.py +++ b/papers/F/Analysis/Real/Cloud/run_craco_real.py @@ -20,8 +20,6 @@ def main( pargs, pfile: str, oproot: str, - NFRB: int = None, - iFRB: int = 0, outdir: str = "Output", ): @@ -112,10 +110,6 @@ def parse_option(): parser.add_argument( "-b", "--batch", type=int, default=1, required=False, help="Batch number" ) - parser.add_argument( - "--NFRB", type=int, required=False, help="Number of FRBs to analzye" - ) - parser.add_argument("--iFRB", type=int, default=0, help="Initial FRB to run from") args = parser.parse_args() return args @@ -126,4 +120,4 @@ def parse_option(): pfile = "../Cubes/craco_real_cube.json" oproot = "craco_real.csv" pargs = parse_option() - main(pargs, pfile, oproot, NFRB=100, iFRB=100) + main(pargs, pfile, oproot) From 9c6291b92f096c4d70a375fa70d94ae271d47446 Mon Sep 17 00:00:00 2001 From: Jay Baptista Date: Wed, 16 Nov 2022 14:29:43 -0500 Subject: [PATCH 078/104] actually fixing it this time --- papers/F/Analysis/Real/Cloud/run_craco_real.py | 11 +---------- 1 file changed, 1 insertion(+), 10 deletions(-) diff --git a/papers/F/Analysis/Real/Cloud/run_craco_real.py b/papers/F/Analysis/Real/Cloud/run_craco_real.py index b61bb237..726ce870 100644 --- a/papers/F/Analysis/Real/Cloud/run_craco_real.py +++ b/papers/F/Analysis/Real/Cloud/run_craco_real.py @@ -17,10 +17,7 @@ def main( - pargs, - pfile: str, - oproot: str, - outdir: str = "Output", + pargs, pfile: str, oproot: str, outdir: str = "Output", ): # Generate the folder? @@ -64,12 +61,6 @@ def main( "-p", f"{pfile}", ] - # NFRB? - if NFRB is not None: - line += [f"--NFRB", f"{NFRB}"] - # iFRB? - if iFRB > 0: - line += [f"--iFRB", f"{iFRB}"] # Finish # line += ' & \n' commands.append(line) From 9197b0afb4d1d09c65269d2ee233390191458de6 Mon Sep 17 00:00:00 2001 From: Jay Baptista Date: Wed, 16 Nov 2022 14:50:44 -0500 Subject: [PATCH 079/104] add missing survey file --- zdm/data/Surveys/CRAFT_ICS_1632.dat | 12 ++++++++++++ 1 file changed, 12 insertions(+) create mode 100644 zdm/data/Surveys/CRAFT_ICS_1632.dat diff --git a/zdm/data/Surveys/CRAFT_ICS_1632.dat b/zdm/data/Surveys/CRAFT_ICS_1632.dat new file mode 100644 index 00000000..a80f245e --- /dev/null +++ b/zdm/data/Surveys/CRAFT_ICS_1632.dat @@ -0,0 +1,12 @@ +BW 336 #MHz +FRES 1 #MHz +NFRB 1 #Number of FRBs +BEAM lat50_log #prefix of beam file. +TOBS 8.07 +NORMFRB 1 +THRESH 4.4 #Jy ms to a 1 ms burst, averaged over THRESH of below configs (22/sqrt{Nant}). Should be adjusted to higher frequency. +SNRTHRESH 9 # signal-to-noise threshold: scales jy ms to snr +KEY ID DM FBAR SNR XRA XDEC DMG XNant XConfig TRES WIDTH Z +FRB 211212 206 1632.5 12.8 10:30:40.7 01:40:36.8 27.1 24 closepack36/45/0.9 1.182 2.7 0.0715 +#FRB 220105 583 1632.5 9.8 13:54:51.4 22:29:19.7 22 22 closepack36/45/0.9 1.182 2.0 -1 + From 8a03932a61130e837fed28f00cef709a1d79f848 Mon Sep 17 00:00:00 2001 From: Jay Baptista Date: Wed, 16 Nov 2022 15:06:24 -0500 Subject: [PATCH 080/104] turn F into logF for real cube json --- .../Analysis/Real/Cubes/craco_real_cube.json | 52 +++++-------------- 1 file changed, 12 insertions(+), 40 deletions(-) diff --git a/papers/F/Analysis/Real/Cubes/craco_real_cube.json b/papers/F/Analysis/Real/Cubes/craco_real_cube.json index 059e43a9..b1a2ea18 100644 --- a/papers/F/Analysis/Real/Cubes/craco_real_cube.json +++ b/papers/F/Analysis/Real/Cubes/craco_real_cube.json @@ -1,7 +1,7 @@ { "state": { "energy": { - "luminosity_function": 2 + "luminosity_function": 3 }, "FRBdemo": { "alpha_method": 1 @@ -13,63 +13,35 @@ "cube": { "parameter_order": [ "lC", - "sfr_n", "lmean", "lsigma", - "F", - "alpha", - "lEmax", - "gamma", + "logF", "H0" ] }, - "lEmax": { - "DC": "energy", - "min": 40.5, - "max": 42.5, - "n": 10 - }, "H0": { "DC": "cosmo", - "min": 55.0, + "min": 60.0, "max": 80.0, - "n": 25 - }, - "alpha": { - "DC": "energy", - "min": 0.2, - "max": 2.0, - "n": 3 - }, - "gamma": { - "DC": "energy", - "min": -0.5, - "max": -1.5, - "n": 5 - }, - "sfr_n": { - "DC": "FRBdemo", - "min": 0.0, - "max": 3.0, - "n": 20 + "n": 21 }, "lmean": { "DC": "host", "min": 1.7, "max": 2.5, - "n": 5 + "n": 10 }, "lsigma": { "DC": "host", - "min": 0.3, - "max": 0.7, - "n": 5 + "min": 0.2, + "max": 0.9, + "n": 10 }, - "F": { + "logF": { "DC": "IGM", - "min": 0.01, - "max": 0.99, - "n": 20 + "min": -1.7, + "max": 0, + "n": 30 }, "lC": { "DC": "FRBdemo", From 3c7946459c2c857124d88ed72089f576445142cd Mon Sep 17 00:00:00 2001 From: Jay Baptista Date: Wed, 16 Nov 2022 15:24:23 -0500 Subject: [PATCH 081/104] experimenting with lower spline limit --- zdm/energetics.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/zdm/energetics.py b/zdm/energetics.py index e63456ef..5da65acc 100644 --- a/zdm/energetics.py +++ b/zdm/energetics.py @@ -39,7 +39,7 @@ def init_igamma_linear(gammas:list, reinit:bool=False, print(f"Initializing igamma_linear for gamma={gamma} with log10") # values - avals = 10**np.linspace(-7.5, 6., 1000) + avals = 10**np.linspace(-7.7, 6., 1000) numer = np.array([float(mpmath.gammainc( gamma, a=iEE)) for iEE in avals]) From bbf0252db16639bd39452a8d6055d38c6d0e3c7a Mon Sep 17 00:00:00 2001 From: Jay Baptista Date: Wed, 16 Nov 2022 15:29:39 -0500 Subject: [PATCH 082/104] lowering energetics threshold by order of mag --- zdm/energetics.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/zdm/energetics.py b/zdm/energetics.py index 5da65acc..a2499ea1 100644 --- a/zdm/energetics.py +++ b/zdm/energetics.py @@ -39,7 +39,7 @@ def init_igamma_linear(gammas:list, reinit:bool=False, print(f"Initializing igamma_linear for gamma={gamma} with log10") # values - avals = 10**np.linspace(-7.7, 6., 1000) + avals = 10**np.linspace(-8, 6., 1000) numer = np.array([float(mpmath.gammainc( gamma, a=iEE)) for iEE in avals]) From d5793adc0ce91769cf167f1b2597ed292cdb31b1 Mon Sep 17 00:00:00 2001 From: Jay Baptista Date: Mon, 21 Nov 2022 21:05:36 -0500 Subject: [PATCH 083/104] fix survey size bug --- papers/F/Analysis/CRACO/Cubes/craco_H0_logF_state.json | 2 +- zdm/survey.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/papers/F/Analysis/CRACO/Cubes/craco_H0_logF_state.json b/papers/F/Analysis/CRACO/Cubes/craco_H0_logF_state.json index bc5f08a8..6eb48772 100644 --- a/papers/F/Analysis/CRACO/Cubes/craco_H0_logF_state.json +++ b/papers/F/Analysis/CRACO/Cubes/craco_H0_logF_state.json @@ -34,7 +34,7 @@ "gamma": -1.01, "lEmax": 41.4, "lEmin": 30, - "luminosity_function": 2 + "luminosity_function": 3 }, "host": { "lmean": 2.18, diff --git a/zdm/survey.py b/zdm/survey.py index bf7c3a91..024e02ac 100644 --- a/zdm/survey.py +++ b/zdm/survey.py @@ -165,7 +165,7 @@ def process_survey_file(self,filename:str, NFRB:int=None, self.frblist=self.find(keys,'FRB') if NFRB is not None: # Take the first set - ensures we do not overrun the total number of FRBs - if self.NFRB < NFRB+iFRB: + if self.NFRB > NFRB+iFRB: raise ValueError("Cannot return sufficient FRBs, did you mean NFRB=None?") themax = min(NFRB+iFRB,self.NFRB) self.frblist=self.frblist[iFRB:themax] From 3a7b152a05b7df1fe6a2152dc4bb52a90be1a087 Mon Sep 17 00:00:00 2001 From: Jay Baptista Date: Tue, 22 Nov 2022 10:33:18 -0500 Subject: [PATCH 084/104] make mini real cube test --- papers/F/Analysis/Real/Cloud/run_real_mini.py | 114 ++++++++++++++++++ .../F/Analysis/Real/Cubes/real_mini_cube.json | 38 ++++++ .../Analysis/Real/Cubes/real_mini_state.json | 57 +++++++++ 3 files changed, 209 insertions(+) create mode 100644 papers/F/Analysis/Real/Cloud/run_real_mini.py create mode 100644 papers/F/Analysis/Real/Cubes/real_mini_cube.json create mode 100644 papers/F/Analysis/Real/Cubes/real_mini_state.json diff --git a/papers/F/Analysis/Real/Cloud/run_real_mini.py b/papers/F/Analysis/Real/Cloud/run_real_mini.py new file mode 100644 index 00000000..dc8f83d7 --- /dev/null +++ b/papers/F/Analysis/Real/Cloud/run_real_mini.py @@ -0,0 +1,114 @@ +""" Run a Nautilus test """ + +# It should be possible to remove all the matplotlib calls from this +# but in the current implementation it is not removed. +import argparse +import numpy as np +import os, sys +from pkg_resources import resource_filename + +from concurrent.futures import ProcessPoolExecutor +import subprocess + +from zdm import iteration as it +from zdm import io + +from IPython import embed + + +def main( + pargs, pfile: str, oproot: str, outdir: str = "Output", +): + + # Generate the folder? + if not os.path.isdir(outdir): + os.mkdir(outdir) + + ############## Load up ############## + input_dict = io.process_jfile(pfile) + + # Deconstruct the input_dict + state_dict, cube_dict, vparam_dict = it.parse_input_dict(input_dict) + + npoints = np.array([item["n"] for key, item in vparam_dict.items()]) + ntotal = int(np.prod(np.abs(npoints))) + + # Total number of CPUs to be running on this Cube + total_ncpu = pargs.ncpu if pargs.total_ncpu is None else pargs.total_ncpu + batch = 1 if pargs.batch is None else pargs.batch + + nper_cpu = ntotal // total_ncpu + if int(ntotal / total_ncpu) != nper_cpu: + raise IOError(f"Ncpu={total_ncpu} must divide evenly into ntotal={ntotal}") + + commands = [] + for kk in range(pargs.ncpu): + line = [] + # Which CPU is running out of the total? + iCPU = (batch - 1) * pargs.ncpu + kk + outfile = os.path.join(outdir, oproot.replace(".csv", f"{iCPU+1}.csv")) + # Command + line = [ + "python", + "../py/build_real_cube.py", + "-n", + f"{iCPU+1}", + "-m", + f"{nper_cpu}", + "-o", + f"{outfile}", + "--clobber", + "-p", + f"{pfile}", + ] + # Finish + # line += ' & \n' + commands.append(line) + + # Launch em! + processes = [] + + for command in commands: + # Popen + print(f"Running this command: {' '.join(command)}") + pw = subprocess.Popen(command) + processes.append(pw) + + # Wait on em! + for pw in processes: + pw.wait() + + print("All done!") + + +def parse_option(): + # test for command-line arguments here + parser = argparse.ArgumentParser() + parser.add_argument( + "-n", + "--ncpu", + type=int, + required=True, + help="Number of CPUs to run on (might be split in batches)", + ) + parser.add_argument( + "-t", + "--total_ncpu", + type=int, + required=False, + help="Total number of CPUs to run on (might be split in batches)", + ) + parser.add_argument( + "-b", "--batch", type=int, default=1, required=False, help="Batch number" + ) + args = parser.parse_args() + + return args + + +if __name__ == "__main__": + # get the argument of training. + pfile = "../Cubes/real_mini_cube.json" + oproot = "mini_real.csv" + pargs = parse_option() + main(pargs, pfile, oproot) diff --git a/papers/F/Analysis/Real/Cubes/real_mini_cube.json b/papers/F/Analysis/Real/Cubes/real_mini_cube.json new file mode 100644 index 00000000..9e2fcefb --- /dev/null +++ b/papers/F/Analysis/Real/Cubes/real_mini_cube.json @@ -0,0 +1,38 @@ +{ + "state": { + "energy": { + "luminosity_function": 3 + }, + "FRBdemo": { + "alpha_method": 1 + }, + "cosmo": { + "fix_Omega_b_h2": true + } + }, + "cube": { + "parameter_order": [ + "lC", + "logF", + "H0" + ] + }, + "H0": { + "DC": "cosmo", + "min": 60.0, + "max": 80.0, + "n": 5 + }, + "logF": { + "DC": "IGM", + "min": -1.7, + "max": 0, + "n": 5 + }, + "lC": { + "DC": "FRBdemo", + "min": -0.911, + "max": -0.911, + "n": -1 + } +} \ No newline at end of file diff --git a/papers/F/Analysis/Real/Cubes/real_mini_state.json b/papers/F/Analysis/Real/Cubes/real_mini_state.json new file mode 100644 index 00000000..6eb48772 --- /dev/null +++ b/papers/F/Analysis/Real/Cubes/real_mini_state.json @@ -0,0 +1,57 @@ +{ + "FRBdemo": { + "alpha_method": 1, + "lC": 1, + "sfr_n": 0.73, + "source_evolution": 0 + }, + "IGM": { + "logF": 0.32 + }, + "MW": { + "DMhalo": 50, + "ISM": 35.0 + }, + "analysis": { + "NewGrids": true, + "sprefix": "Std" + }, + "beam": { + "Bmethod": 2, + "Bthresh": 0 + }, + "cosmo": { + "H0": 67.66, + "Omega_b": 0.04897, + "Omega_b_h2": 0.0224178568132, + "Omega_k": 0.0, + "Omega_lambda": 0.6888463055445441, + "Omega_m": 0.30966, + "fix_Omega_b_h2": true + }, + "energy": { + "alpha": 0.65, + "gamma": -1.01, + "lEmax": 41.4, + "lEmin": 30, + "luminosity_function": 3 + }, + "host": { + "lmean": 2.18, + "lsigma": 0.48 + }, + "scat": { + "Sfnorm": 600, + "Sfpower": -4.0, + "Slogmean": 0.7, + "Slogsigma": 1.9 + }, + "width": { + "Wbins": 10, + "Wlogmean": 1.70267, + "Wlogsigma": 0.899148, + "Wmethod": 2, + "Wscale": 2, + "Wthresh": 0.5 + } +} \ No newline at end of file From 2b80554bcfe4779089db4ebd972fdc4647644b96 Mon Sep 17 00:00:00 2001 From: Jay Baptista Date: Tue, 22 Nov 2022 16:09:22 -0500 Subject: [PATCH 085/104] set nz=5000 to nz=500 --- .../CRACO/Cloud/nautilus_craco_tiny_logF.yaml | 80 ++++++++++++++++++ .../Real/Cloud/nautilus_real_mini.yaml | 82 +++++++++++++++++++ papers/F/Analysis/Real/Cloud/run_real_mini.py | 3 +- zdm/real_loading.py | 2 +- 4 files changed, 165 insertions(+), 2 deletions(-) create mode 100644 papers/F/Analysis/CRACO/Cloud/nautilus_craco_tiny_logF.yaml create mode 100644 papers/F/Analysis/Real/Cloud/nautilus_real_mini.yaml diff --git a/papers/F/Analysis/CRACO/Cloud/nautilus_craco_tiny_logF.yaml b/papers/F/Analysis/CRACO/Cloud/nautilus_craco_tiny_logF.yaml new file mode 100644 index 00000000..f9293ff3 --- /dev/null +++ b/papers/F/Analysis/CRACO/Cloud/nautilus_craco_tiny_logF.yaml @@ -0,0 +1,80 @@ +# 25 processors on mini for Varying F +# kubectl exec -it test-pod -- /bin/bash +apiVersion: batch/v1 +kind: Job +metadata: + name: jay-zdm-craco-tiny-logf +spec: + backoffLimit: 0 + template: + spec: + affinity: + nodeAffinity: + requiredDuringSchedulingIgnoredDuringExecution: + nodeSelectorTerms: + - matchExpressions: + - key: kubernetes.io/hostname + operator: NotIn + values: + - k8s-chase-ci-01.noc.ucsb.edu + - key: nvidia.com/gpu.product + operator: In + values: + - NVIDIA-GeForce-GTX-1080-Ti + containers: + - name: container + image: localhost:30081/profxj/zdm_docker:latest # UPDATE + imagePullPolicy: Always + resources: + requests: + cpu: "25" + memory: "8Gi" # + ephemeral-storage: 50Gi # + limits: + cpu: "27" + memory: "12Gi" + ephemeral-storage: 100Gi + #nvidia.com/gpu: "1" # See docs to exlude certain types + command: ["/bin/bash", "-c"] + args: + - cd FRB; + git fetch; + git pull; + python setup.py develop; + cd ../ne2001; + python setup.py develop; + cd ../zdm; + git fetch; + git checkout varying_F; + python setup.py develop; + cd papers/F/Analysis/CRACO/Cloud; + python run_craco_H0_logF.py -n 25 -t 25 -b 1; + aws --endpoint http://rook-ceph-rgw-nautiluss3.rook s3 cp Output s3://zdm/Cubes/F/full/ --recursive --force; + env: + - name: "ENDPOINT_URL" + value: "http://rook-ceph-rgw-nautiluss3.rook" + - name: "S3_ENDPOINT" + value: "rook-ceph-rgw-nautiluss3.rook" + volumeMounts: + - name: prp-s3-credentials + mountPath: "/root/.aws/credentials" + subPath: "credentials" + - name: ephemeral + mountPath: "/tmp" + - name: "dshm" + mountPath: "/dev/shm" + nodeSelector: + nautilus.io/disktype: nvme + restartPolicy: Never + volumes: + # Secrets file for nautilus s3 credentials .aws/credentials and .s3cfg + - name: prp-s3-credentials + secret: + secretName: prp-s3-credentials + # Shared memory (necessary for Python's multiprocessing.shared_memory module to work) + - name: dshm + emptyDir: + medium: Memory + # Ephemeral storage + - name: ephemeral + emptyDir: {} diff --git a/papers/F/Analysis/Real/Cloud/nautilus_real_mini.yaml b/papers/F/Analysis/Real/Cloud/nautilus_real_mini.yaml new file mode 100644 index 00000000..4aeadedd --- /dev/null +++ b/papers/F/Analysis/Real/Cloud/nautilus_real_mini.yaml @@ -0,0 +1,82 @@ +# 21 processors on full for Varying F +# kubectl exec -it test-pod -- /bin/bash +apiVersion: batch/v1 +kind: Job +metadata: + name: jb-zdm-craco-real-logf-mini-v2 +spec: + backoffLimit: 0 + template: + spec: + affinity: + nodeAffinity: + requiredDuringSchedulingIgnoredDuringExecution: + nodeSelectorTerms: + - matchExpressions: + - key: kubernetes.io/hostname + operator: NotIn + values: + - k8s-chase-ci-01.noc.ucsb.edu + - key: nvidia.com/gpu.product + operator: In + values: + - NVIDIA-GeForce-GTX-1080-Ti + containers: + - name: container + image: localhost:30081/profxj/zdm_docker:latest # UPDATE + imagePullPolicy: Always + resources: + requests: + cpu: "21" + memory: "16Gi" # + ephemeral-storage: 50Gi # + limits: + cpu: "23" + memory: "24Gi" + ephemeral-storage: 100Gi + #nvidia.com/gpu: "1" # See docs to exlude certain types + command: ["/bin/bash", "-c"] + args: + - cd FRB; + git fetch; + git pull; + python setup.py develop; + cd ../ne2001; + python setup.py develop; + cd ../zdm; + git fetch; + git checkout varying_F; + python setup.py develop; + cd papers/F/Analysis/Real/Cloud; + mkdir Output; + python run_real_mini.py -n 5 -t 5 -b 1; + aws --endpoint http://rook-ceph-rgw-nautiluss3.rook s3 cp Output s3://zdm/Cubes/F/full/ --recursive --force; + sleep 600; + env: + - name: "ENDPOINT_URL" + value: "http://rook-ceph-rgw-nautiluss3.rook" + - name: "S3_ENDPOINT" + value: "rook-ceph-rgw-nautiluss3.rook" + volumeMounts: + - name: prp-s3-credentials + mountPath: "/root/.aws/credentials" + subPath: "credentials" + - name: ephemeral + mountPath: "/tmp" + - name: "dshm" + mountPath: "/dev/shm" + nodeSelector: + nautilus.io/disktype: nvme + restartPolicy: Never + volumes: + # Secrets file for nautilus s3 credentials .aws/credentials and .s3cfg + - name: prp-s3-credentials + secret: + secretName: prp-s3-credentials + # Shared memory (necessary for Python's multiprocessing.shared_memory module to work) + - name: dshm + emptyDir: + medium: Memory + # Ephemeral storage + - name: ephemeral + emptyDir: {} diff --git a/papers/F/Analysis/Real/Cloud/run_real_mini.py b/papers/F/Analysis/Real/Cloud/run_real_mini.py index dc8f83d7..aa137160 100644 --- a/papers/F/Analysis/Real/Cloud/run_real_mini.py +++ b/papers/F/Analysis/Real/Cloud/run_real_mini.py @@ -76,7 +76,8 @@ def main( # Wait on em! for pw in processes: - pw.wait() + exit_code = pw.wait() + print(exit_code) print("All done!") diff --git a/zdm/real_loading.py b/zdm/real_loading.py index 6454c9d8..47872b80 100644 --- a/zdm/real_loading.py +++ b/zdm/real_loading.py @@ -114,7 +114,7 @@ def surveys_and_grids(init_state=None, alpha_method=1, add_20220610A=False): # get the grid of p(DM|z) zDMgrid, zvals,dmvals = misc_functions.get_zdm_grid( - state, new=True, plot=False, method='analytic', nz=5000, + state, new=True, plot=False, method='analytic', nz=500, datdir=resource_filename('zdm', 'GridData')) ############## Initialise surveys ############## From cf4a8d888e09d377685e21609a7d8b1de2cc5830 Mon Sep 17 00:00:00 2001 From: Jay Baptista Date: Tue, 22 Nov 2022 22:56:54 -0500 Subject: [PATCH 086/104] modify memory usage for real run --- papers/F/Analysis/Real/Cloud/nautilus_real_cube.yaml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/papers/F/Analysis/Real/Cloud/nautilus_real_cube.yaml b/papers/F/Analysis/Real/Cloud/nautilus_real_cube.yaml index 8338c817..35bb1eb3 100644 --- a/papers/F/Analysis/Real/Cloud/nautilus_real_cube.yaml +++ b/papers/F/Analysis/Real/Cloud/nautilus_real_cube.yaml @@ -28,11 +28,11 @@ spec: resources: requests: cpu: "21" - memory: "8Gi" # + memory: "25Gi" # ephemeral-storage: 50Gi # limits: cpu: "23" - memory: "12Gi" + memory: "50Gi" ephemeral-storage: 100Gi #nvidia.com/gpu: "1" # See docs to exlude certain types command: ["/bin/bash", "-c"] @@ -50,7 +50,7 @@ spec: cd papers/F/Analysis/Real/Cloud; mkdir Output; python run_craco_real.py -n 21 -t 21 -b 1; - aws --endpoint http://rook-ceph-rgw-nautiluss3.rook s3 cp Output s3://zdm/Cubes/F/full/ --recursive --force; + aws --endpoint http://rook-ceph-rgw-nautiluss3.rook s3 cp Output s3://zdm/Cubes/F/real/ --recursive --force; env: - name: "ENDPOINT_URL" value: "http://rook-ceph-rgw-nautiluss3.rook" From 02a5bbdf9e9b70b68b8295a7bdfd5c6f4958831f Mon Sep 17 00:00:00 2001 From: Jay Baptista Date: Fri, 27 Jan 2023 22:26:19 -0500 Subject: [PATCH 087/104] bug fixes, analyzing 2022 private surveys --- .gitignore | 3 + papers/F/Analysis/CRACO/2d.ipynb | 17 +- papers/F/Analysis/CRACO/make_fig10.py | 59 + papers/F/Analysis/CRACO/marginalize.ipynb | 31 +- .../Analysis/Real/Cubes/craco_real_state.json | 57 + .../Analysis/Real/logF_host_comparison.ipynb | 132 +++ .../Real/logF_sigma_comparison copy.ipynb | 109 ++ .../Analysis/Real/logF_sigma_comparison.ipynb | 132 +++ papers/F/Analysis/Real/make_fig6.py | 222 ++++ papers/F/Analysis/Real/make_fig7.py | 186 +++ .../F/Analysis/Real/py/craco_qck_explore.py | 81 ++ .../F/Analysis/Real/py/slurp_craco_cubes.py | 175 +++ papers/F/Analysis/Real/testF.py | 138 +++ papers/F/Analysis/Real/tmp.ipynb | 150 +++ papers/F/Analysis/py/analy_F_I.py | 2 +- papers/F/Figures/py/figs_compare.py | 160 +++ papers/F/Figures/py/figs_zdm_F_I.py | 46 +- zdm/analyze_cube.py | 9 +- zdm/craco/MC_F/Surveys/F_0.32_survey.ecsv | 1024 +++++++++++++++++ zdm/grid.py | 2 +- zdm/real_loading.py | 8 +- zdm/scripts/plot_limits_from_cube.py | 1 + zdm/survey.py | 21 +- 23 files changed, 2699 insertions(+), 66 deletions(-) create mode 100644 papers/F/Analysis/CRACO/make_fig10.py create mode 100644 papers/F/Analysis/Real/Cubes/craco_real_state.json create mode 100644 papers/F/Analysis/Real/logF_host_comparison.ipynb create mode 100644 papers/F/Analysis/Real/logF_sigma_comparison copy.ipynb create mode 100644 papers/F/Analysis/Real/logF_sigma_comparison.ipynb create mode 100644 papers/F/Analysis/Real/make_fig6.py create mode 100644 papers/F/Analysis/Real/make_fig7.py create mode 100644 papers/F/Analysis/Real/py/craco_qck_explore.py create mode 100644 papers/F/Analysis/Real/py/slurp_craco_cubes.py create mode 100644 papers/F/Analysis/Real/testF.py create mode 100644 papers/F/Analysis/Real/tmp.ipynb create mode 100644 papers/F/Figures/py/figs_compare.py create mode 100644 zdm/craco/MC_F/Surveys/F_0.32_survey.ecsv diff --git a/.gitignore b/.gitignore index ababfd94..5563859b 100644 --- a/.gitignore +++ b/.gitignore @@ -172,3 +172,6 @@ zdm/untitled10.py papers/H0_I/Analysis/Real/minicube_real.src +zdm/data/Surveys/private_CRAFT_ICS_892.ecsv +zdm/data/Surveys/private_CRAFT_ICS_1272.ecsv +zdm/data/Surveys/private_CRAFT_ICS_1632.ecsv diff --git a/papers/F/Analysis/CRACO/2d.ipynb b/papers/F/Analysis/CRACO/2d.ipynb index b2f4c187..d4879c02 100644 --- a/papers/F/Analysis/CRACO/2d.ipynb +++ b/papers/F/Analysis/CRACO/2d.ipynb @@ -75,7 +75,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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\n", 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", 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" ] @@ -149,7 +149,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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", "text/plain": [ "
" ] @@ -214,7 +214,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3.8.5 ('base')", "language": "python", "name": "python3" }, @@ -228,7 +228,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.10" + "version": "3.8.5 (default, Sep 4 2020, 02:22:02) \n[Clang 10.0.0 ]" + }, + "vscode": { + "interpreter": { + "hash": "9731a3b7ebb41545a410367c249afbc4842fd5740da82f1bd29e6ac1d7253e6e" + } } }, "nbformat": 4, diff --git a/papers/F/Analysis/CRACO/make_fig10.py b/papers/F/Analysis/CRACO/make_fig10.py new file mode 100644 index 00000000..6712538b --- /dev/null +++ b/papers/F/Analysis/CRACO/make_fig10.py @@ -0,0 +1,59 @@ +""" +Plots Figure 10 ('CRACO') analysis + +Produces plots for each parameter, even though only H0 +was shown in the paper. + +""" + +import numpy as np +import os +import zdm +from zdm import analyze_cube as ac + +from matplotlib import pyplot as plt + +def main(): + + if not os.path.exists("Figure10/"): + os.mkdir("Figure10") + + CubeFile='Cubes/craco_full_cube.npz' + if os.path.exists(CubeFile): + data=np.load(CubeFile) + else: + print("Missing cube file ",CubeFile," please download") + exit() + + data=np.load(CubeFile) + + lst = data.files + lldata=data["ll"] + params=data["params"] + # builds uvals list + uvals=[] + for param in params: + uvals.append(data[param]) + + deprecated,vectors,wvectors=ac.get_bayesian_data(data["ll"]) + + latexnames=[ + "H_0", + "\\mu_{\\rm host}", + "\\sigma_{\\rm host}", + "\\log_{10} F", + ] + units=[ + "km/s/Mpc", + "", + "", + "", + ] + + # ['[erg]','[km/s/Mpc]','','','','$[\\log_{10} {\\rm DM}]',''] + + truth=[67.66,2.16,.51,-0.49] + #ac.do_single_plots(uvals,vectors,wvectors,params,tag="prior_",truth=truth,dolevels=True,latexnames=latexnames) + ac.do_single_plots(uvals,vectors,None,params,tag="Figure10_",truth=truth,dolevels=True,latexnames=latexnames,units=units) + +main() diff --git a/papers/F/Analysis/CRACO/marginalize.ipynb b/papers/F/Analysis/CRACO/marginalize.ipynb index e66d60a9..a8b2cc2b 100644 --- a/papers/F/Analysis/CRACO/marginalize.ipynb +++ b/papers/F/Analysis/CRACO/marginalize.ipynb @@ -66,28 +66,6 @@ " print(key)" ] }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([55. , 56.04166667, 57.08333333, 58.125 , 59.16666667,\n", - " 60.20833333, 61.25 , 62.29166667, 63.33333333, 64.375 ,\n", - " 65.41666667, 66.45833333, 67.5 , 68.54166667, 69.58333333,\n", - " 70.625 , 71.66666667, 72.70833333, 73.75 , 74.79166667,\n", - " 75.83333333, 76.875 , 77.91666667, 78.95833333, 80. ])" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [] - }, { "cell_type": "code", "execution_count": 15, @@ -132,13 +110,6 @@ "ax.set_ylim(0, 0.05)\n", "plt.show()" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { @@ -157,7 +128,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.10" + "version": "3.8.5" }, "vscode": { "interpreter": { diff --git a/papers/F/Analysis/Real/Cubes/craco_real_state.json b/papers/F/Analysis/Real/Cubes/craco_real_state.json new file mode 100644 index 00000000..6eb48772 --- /dev/null +++ b/papers/F/Analysis/Real/Cubes/craco_real_state.json @@ -0,0 +1,57 @@ +{ + "FRBdemo": { + "alpha_method": 1, + "lC": 1, + "sfr_n": 0.73, + "source_evolution": 0 + }, + "IGM": { + "logF": 0.32 + }, + "MW": { + "DMhalo": 50, + "ISM": 35.0 + }, + "analysis": { + "NewGrids": true, + "sprefix": "Std" + }, + "beam": { + "Bmethod": 2, + "Bthresh": 0 + }, + "cosmo": { + "H0": 67.66, + "Omega_b": 0.04897, + "Omega_b_h2": 0.0224178568132, + "Omega_k": 0.0, + "Omega_lambda": 0.6888463055445441, + "Omega_m": 0.30966, + "fix_Omega_b_h2": true + }, + "energy": { + "alpha": 0.65, + "gamma": -1.01, + "lEmax": 41.4, + "lEmin": 30, + "luminosity_function": 3 + }, + "host": { + "lmean": 2.18, + "lsigma": 0.48 + }, + "scat": { + "Sfnorm": 600, + "Sfpower": -4.0, + "Slogmean": 0.7, + "Slogsigma": 1.9 + }, + "width": { + "Wbins": 10, + "Wlogmean": 1.70267, + "Wlogsigma": 0.899148, + "Wmethod": 2, + "Wscale": 2, + "Wthresh": 0.5 + } +} \ No newline at end of file diff --git a/papers/F/Analysis/Real/logF_host_comparison.ipynb b/papers/F/Analysis/Real/logF_host_comparison.ipynb new file mode 100644 index 00000000..a5ea630f --- /dev/null +++ b/papers/F/Analysis/Real/logF_host_comparison.ipynb @@ -0,0 +1,132 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import zdm.analyze_cube as ac\n", + "import matplotlib.pyplot as plt\n", + "import zdm.analyze_cube as ac\n", + "\n", + "cube_dir_real = \"./Cubes/craco_real_cube.npz\"\n", + "cube_dir_full = \"../CRACO/Cubes/craco_full_cube.npz\"\n", + "\n", + "cube_real = np.load(cube_dir_real)\n", + "cube_full = np.load(cube_dir_full)\n", + "\n", + "lls_real = ac.get_slice_from_parameters(cube_real, [\"H0\", \"lsigma\"], [73, .51], verbose=False, wanted=\"ll\")\n", + "lls_full = ac.get_slice_from_parameters(cube_full, [\"H0\", \"lsigma\"], [73, .51], verbose=False, wanted=\"ll\")\n", + "\n", + "lls_real -= np.max(lls_real)\n", + "lls_real = 10**lls_real\n", + "lls_real /= np.sum(lls_real)\n", + "\n", + "lls_full -= np.max(lls_full)\n", + "lls_full = 10**lls_full\n", + "lls_full /= np.sum(lls_full)\n", + "\n", + "means, fs = np.meshgrid(cube_real[\"lmean\"], cube_real[\"logF\"])\n", + "\n", + "fig, ax = plt.subplots(1, 2, dpi=200, figsize=(12,5))\n", + "\n", + "f_full = ax[0].pcolormesh(means, fs, lls_full.T, shading=\"nearest\")\n", + "ax[0].set_xlabel(r\"$\\mathrm{DM_{host}}$\")\n", + "ax[0].set_ylabel(r\"$\\log_{10} F$\")\n", + "max_idx_i, max_idx_j = np.where(lls_full == lls_full.max())\n", + "ax[0].scatter(cube_real[\"lmean\"][max_idx_i], cube_real[\"logF\"][max_idx_j], c='red', marker='x', label=\"max\")\n", + "ax[0].legend()\n", + "ax[0].axhline(np.log10(0.32), c='k', ls='--', alpha=.25)\n", + "ax[0].axvline(2.16, c='k', ls='--', alpha=.25)\n", + "ax[0].set_title(\"CRACO Full Cube\")\n", + "plt.colorbar(f_full, label=r\"$\\log \\mathcal{L}$\", ax=ax[0])\n", + "\n", + "f_real = ax[1].pcolormesh(means, fs, lls_real.T, shading=\"nearest\")\n", + "ax[1].set_xlabel(r\"$\\mathrm{DM_{host}}$\")\n", + "ax[1].set_ylabel(r\"$\\log_{10} F$\")\n", + "max_idx_i, max_idx_j = np.where(lls_real == lls_real.max())\n", + "ax[1].scatter(cube_real[\"lmean\"][max_idx_i], cube_real[\"logF\"][max_idx_j], c='red', marker='x', label=\"max\")\n", + "ax[1].legend()\n", + "ax[1].axhline(np.log10(0.32), c='k', ls='--', alpha=.25)\n", + "ax[1].axvline(2.16, c='k', ls='--', alpha=.25)\n", + "ax[1].set_title(\"Real Cube\")\n", + "\n", + "fig.tight_layout()\n", + "plt.colorbar(f_real, label=r\"$\\log \\mathcal{L}$\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "for i in np.arange(len(cube_real[\"lsigma\"])):\n", + " plt.plot(cube_real[\"logF\"], lls_real[i, :])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.8.5 ('base')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5 (default, Sep 4 2020, 02:22:02) \n[Clang 10.0.0 ]" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "9731a3b7ebb41545a410367c249afbc4842fd5740da82f1bd29e6ac1d7253e6e" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/papers/F/Analysis/Real/logF_sigma_comparison copy.ipynb b/papers/F/Analysis/Real/logF_sigma_comparison copy.ipynb new file mode 100644 index 00000000..799fd574 --- /dev/null +++ b/papers/F/Analysis/Real/logF_sigma_comparison copy.ipynb @@ -0,0 +1,109 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import zdm.analyze_cube as ac\n", + "import matplotlib.pyplot as plt\n", + "import zdm.analyze_cube as ac\n", + "\n", + "cube_dir_real = \"./Cubes/craco_real_cube.npz\"\n", + "cube_dir_full = \"../CRACO/Cubes/craco_full_cube.npz\"\n", + "\n", + "cube_real = np.load(cube_dir_real)\n", + "cube_full = np.load(cube_dir_full)\n", + "\n", + "lls_real = ac.get_slice_from_parameters(cube_real, [\"logF\", \"lmean\"], [-.49, 2.16], verbose=False, wanted=\"ll\")\n", + "lls_full = ac.get_slice_from_parameters(cube_full, [\"logF\", \"lmean\"], [-.49, 2.16], verbose=False, wanted=\"ll\")\n", + "\n", + "lls_real -= np.max(lls_real)\n", + "lls_real = 10**lls_real\n", + "lls_real /= np.sum(lls_real)\n", + "\n", + "lls_full -= np.max(lls_full)\n", + "lls_full = 10**lls_full\n", + "lls_full /= np.sum(lls_full)\n", + "\n", + "sigmas, H0s = np.meshgrid(cube_real[\"lsigma\"], cube_real[\"H0\"])\n", + "\n", + "fig, ax = plt.subplots(1, 2, dpi=200, figsize=(12,5))\n", + "\n", + "f_full = ax[0].pcolormesh(sigmas, H0s, lls_full, shading=\"nearest\")\n", + "ax[0].set_xlabel(r\"$\\sigma_\\mathrm{host}$\")\n", + "ax[0].set_ylabel(r\"$\\log_{10} F$\")\n", + "max_idx_i, max_idx_j = np.where(lls_full == lls_full.max())\n", + "ax[0].scatter(cube_real[\"lsigma\"][max_idx_i], cube_real[\"H0\"][max_idx_j], c='red', marker='x', label=\"max\")\n", + "ax[0].legend()\n", + "ax[0].axhline(64, c='k', ls='--', alpha=.25)\n", + "ax[0].axvline(0.51, c='k', ls='--', alpha=.25)\n", + "ax[0].set_title(\"CRACO Full Cube\")\n", + "plt.colorbar(f_full, label=r\"$\\log \\mathcal{L}$\", ax=ax[0])\n", + "\n", + "f_real = ax[1].pcolormesh(sigmas, H0s, lls_real, shading=\"nearest\")\n", + "ax[1].set_xlabel(r\"$\\sigma_\\mathrm{host}$\")\n", + "ax[1].set_ylabel(r\"$\\log_{10} F$\")\n", + "max_idx_i, max_idx_j = np.where(lls_real == lls_real.max())\n", + "ax[1].scatter(cube_real[\"lsigma\"][max_idx_i], cube_real[\"H0\"][max_idx_j], c='red', marker='x', label=\"max\")\n", + "ax[1].legend()\n", + "ax[1].axhline(64, c='k', ls='--', alpha=.25)\n", + "ax[1].axvline(0.51, c='k', ls='--', alpha=.25)\n", + "ax[1].set_title(\"Real Cube\")\n", + "\n", + "fig.tight_layout()\n", + "plt.colorbar(f_real, label=r\"$\\log \\mathcal{L}$\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.8.5 ('base')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "9731a3b7ebb41545a410367c249afbc4842fd5740da82f1bd29e6ac1d7253e6e" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/papers/F/Analysis/Real/logF_sigma_comparison.ipynb b/papers/F/Analysis/Real/logF_sigma_comparison.ipynb new file mode 100644 index 00000000..1a10b562 --- /dev/null +++ b/papers/F/Analysis/Real/logF_sigma_comparison.ipynb @@ -0,0 +1,132 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import zdm.analyze_cube as ac\n", + "import matplotlib.pyplot as plt\n", + "import zdm.analyze_cube as ac\n", + "\n", + "cube_dir_real = \"./Cubes/craco_real_cube.npz\"\n", + "cube_dir_full = \"../CRACO/Cubes/craco_full_cube.npz\"\n", + "\n", + "cube_real = np.load(cube_dir_real)\n", + "cube_full = np.load(cube_dir_full)\n", + "\n", + "lls_real = ac.get_slice_from_parameters(cube_real, [\"H0\", \"lmean\"], [73, 2.16], verbose=False, wanted=\"ll\")\n", + "lls_full = ac.get_slice_from_parameters(cube_full, [\"H0\", \"lmean\"], [73, 2.16], verbose=False, wanted=\"ll\")\n", + "\n", + "lls_real -= np.max(lls_real)\n", + "lls_real = 10**lls_real\n", + "lls_real /= np.sum(lls_real)\n", + "\n", + "lls_full -= np.max(lls_full)\n", + "lls_full = 10**lls_full\n", + "lls_full /= np.sum(lls_full)\n", + "\n", + "sigmas, fs = np.meshgrid(cube_real[\"lsigma\"], cube_real[\"logF\"])\n", + "\n", + "fig, ax = plt.subplots(1, 2, dpi=200, figsize=(12,5))\n", + "\n", + "f_full = ax[0].pcolormesh(sigmas, fs, lls_full.T, shading=\"nearest\")\n", + "ax[0].set_xlabel(r\"$\\sigma_\\mathrm{host}$\")\n", + "ax[0].set_ylabel(r\"$\\log_{10} F$\")\n", + "max_idx_i, max_idx_j = np.where(lls_full == lls_full.max())\n", + "ax[0].scatter(cube_real[\"lsigma\"][max_idx_i], cube_real[\"logF\"][max_idx_j], c='red', marker='x', label=\"max\")\n", + "ax[0].legend()\n", + "ax[0].axhline(np.log10(0.32), c='k', ls='--', alpha=.25)\n", + "ax[0].axvline(0.51, c='k', ls='--', alpha=.25)\n", + "ax[0].set_title(\"CRACO Full Cube\")\n", + "plt.colorbar(f_full, label=r\"$\\log \\mathcal{L}$\", ax=ax[0])\n", + "\n", + "f_real = ax[1].pcolormesh(sigmas, fs, lls_real.T, shading=\"nearest\")\n", + "ax[1].set_xlabel(r\"$\\sigma_\\mathrm{host}$\")\n", + "ax[1].set_ylabel(r\"$\\log_{10} F$\")\n", + "max_idx_i, max_idx_j = np.where(lls_real == lls_real.max())\n", + "ax[1].scatter(cube_real[\"lsigma\"][max_idx_i], cube_real[\"logF\"][max_idx_j], c='red', marker='x', label=\"max\")\n", + "ax[1].legend()\n", + "ax[1].axhline(np.log10(0.32), c='k', ls='--', alpha=.25)\n", + "ax[1].axvline(0.51, c='k', ls='--', alpha=.25)\n", + "ax[1].set_title(\"Real Cube\")\n", + "\n", + "fig.tight_layout()\n", + "plt.colorbar(f_real, label=r\"$\\log \\mathcal{L}$\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "for i in np.arange(len(cube_real[\"lsigma\"])):\n", + " plt.plot(cube_real[\"logF\"], lls_real[i, :])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.8.5 ('base')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "9731a3b7ebb41545a410367c249afbc4842fd5740da82f1bd29e6ac1d7253e6e" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/papers/F/Analysis/Real/make_fig6.py b/papers/F/Analysis/Real/make_fig6.py new file mode 100644 index 00000000..9fa3a9d5 --- /dev/null +++ b/papers/F/Analysis/Real/make_fig6.py @@ -0,0 +1,222 @@ +""" +Makes figure 6 by fitting H0 outside the analysed range + +""" + +import numpy as np +import os +import zdm +from zdm import analyze_cube as ac + +from matplotlib import pyplot as plt +from scipy.optimize import curve_fit +from scipy.interpolate import interp1d +from scipy.integrate import quad + +from IPython import embed + +def main(): + + # saves values for H0 and marginalised p(H0) posteriors + if not (os.path.isfile("H0.npy") and os.path.isfile("pH0.npy") + and os.path.isfile("ph0_others_all_fixed.npy")): + get_H0_values() + + orig_H0=np.load("H0.npy") + orig_pH0=np.load("pH0.npy") + + # sets the range for fitting the tail of H0 + minH0=76 + maxH0=130 + OK=np.where(orig_H0>=minH0)[0] + H0=orig_H0[OK] + pH0=orig_pH0[OK] + + embed() + # lognormal fit + p0=[1.,1.,1.] + popt,pcov=curve_fit(ln,H0,pH0,p0=p0) + x=np.linspace(minH0,maxH0) + lnx=ln(x,*popt) + lnchisqr=np.sum((ln(H0,*popt)-pH0)**2) + + #spline interpolation + spl=interp1d(orig_H0,orig_pH0,kind='cubic') + longx=np.linspace(orig_H0[0],orig_H0[-1],100) + + # performs integration + p1=quad(spl,orig_H0[0],orig_H0[-1]) + #print("Integral over original range of using a spline comes to ",p1) + + p2=quad(ln,orig_H0[-1],maxH0+50.,args=(popt[0],popt[1],popt[2])) + #print("Integrating from ",orig_H0[-1]," to ",maxH0," gives an additional ",p2) + + # this figures shows a check where the lognormal fit is overplotted on the data + if not os.path.isdir('Figure6'): + os.makedirs("Figure6") + + plt.figure() + plt.scatter(orig_H0,orig_pH0,label="Data") + plt.scatter(H0,pH0, label="fitted data") + plt.plot(longx,spl(longx),label='Spline interpolation') + plt.plot(x,lnx,label="Lognormal extension",linestyle="--") + + plt.xlabel('$H_0$ [km/s/Mpc]') + plt.ylabel('$p(H_0)$') + plt.savefig("Figure6/check_fit.png", dpi=300) + plt.close() + + # renormalises data - p1 was original sum, p2 is new sum + norm=p1[0]+p2[0] + p1 = p1[0]/norm + p2 = p2[0]/norm + #print("Now ratios are ",p1,p2) + + # constructs single vector + nH=1000 #number of H0 points to sample at + xtotal=np.linspace(orig_H0[0],130.,nH) + lower=np.where(xtotal <= orig_H0[-1])[0] + upper=np.where(xtotal > orig_H0[-1])[0] + ytotal=np.zeros([nH]) + ytotal[lower]=spl(xtotal[lower])/norm + ytotal[upper]=ln(xtotal[upper],*popt)/norm + + # makes cumulative distribution + cy=np.cumsum(ytotal) + #print("Approx norm is ",cy[-1]*(xtotal[1]-xtotal[0])) + cy /= cy[-1] + + #orders data + asyt=np.argsort(ytotal) + syt=np.sort(ytotal) + csyt=np.cumsum(ytotal) + csyt /= csyt[-1] + + # values at which to calculate confidence levels + # (1-99.7)/2, (1-95)/2, (1-90)/2, (1-68)/2 (but to greater accuracy) + levels=np.array([0.00135,0.0228,0.05,0.15866]) + + labels=['99.7%','95%','90%','68%'] + linestyles=["--",":","-.","-"] + extrax=np.linspace(orig_H0[-1],maxH0,100) + + plt.figure() + plt.scatter(orig_H0,orig_pH0/norm,label="Data") + #plt.scatter(H0,pH0/norm, label="fitted data") + plt.plot(longx,spl(longx)/norm,label='Spline interpolation') + plt.plot(extrax,ln(extrax,*popt)/norm,label="Log-normal extension",linestyle="--") + + # gets pH0 when all other values fixed + other_H0=np.load('ph0_others_all_fixed.npy') + other_H0=other_H0[0] + spl=interp1d(orig_H0,other_H0,kind='cubic') + othery=spl(longx) + plt.plot(longx,othery,label="Fixed parameters",linestyle=":",color="black") + + plt.xlabel('$H_0$ [km/s/Mpc]') + plt.ylabel('$p(H_0)$') + + for i,l in enumerate(levels): + v1,v2,i1,i2=ac.extract_limits(xtotal,ytotal,l) + plt.plot([xtotal[i1],xtotal[i1]],[0.,ytotal[i1]],color="red",linestyle=linestyles[i]) + plt.plot([xtotal[i2],xtotal[i2]],[0.,ytotal[i2]],color="red",linestyle=linestyles[i]) + plt.text(xtotal[i1]-2,ytotal[i1]+1e-3,labels[i],rotation=90) + plt.text(xtotal[i2],ytotal[i2]+1e-3,labels[i],rotation=90) + + print("limits ",l,v1,v2) + + plt.gca().set_ylim(bottom=0) + plt.legend() + plt.tight_layout() + plt.savefig("Figure6/H0_fig6.png", dpi=300) + plt.close() + print("Wrote: Figure6/H0_fig6.png") + +def ln(x,*params): + a=params[0] + b=params[1] + c=params[2] + lnx=np.log(x) + vals=a*np.exp(-0.5*(lnx-b)**2/c)/x + return vals + +def exp(x,*params): + a=params[0] + b=params[1] + c=params[2] + vals = a*np.exp(-(x-c)/b) + return vals + +def get_H0_fixed_vales(): + + deprecated,uw_vectors,wvectors=ac.get_bayesian_data(data["ll"]) + + + +def get_H0_values(): + CubeFile='Cubes/craco_real_cube.npz' + if os.path.exists(CubeFile): + data=np.load(CubeFile) + else: + print("Could not file cube output file ",CubeFile) + print("Please obtain it from [repository]") + exit() + + lst = data.files + params=data["params"] + + param_vals = [] + param_list = [ + data["H0"], + data["lmean"], + data["lsigma"], + data["logF"] + ] + + for col in param_list: + unique = np.unique(col) + param_vals.append(unique) + + iH0=np.where(data["params"] == "H0") + ################ gets 1D H0 values ############ + deprecated,uw_vectors,wvectors=ac.get_bayesian_data(data["ll"]) + + print("H0: ",param_vals[iH0[0][0]]) + print("Probs: ",uw_vectors[iH0[0][0]]) + np.save("H0.npy",param_vals[iH0[0][0]]) + np.save("pH0.npy",uw_vectors[iH0[0][0]]) + + # builds uvals list, i.e. of unique values of parameters + uvals=[] + for ip,param in enumerate(data["params"]): + # switches for alpha + if param=="alpha": + uvals.append(data[param]*-1.) + else: + uvals.append(data[param]) + + # extract the best-fit parameter values + list2=[] + vals2=[] + for i,vec in enumerate(uw_vectors): + n=np.argmax(vec) + val=uvals[i][n] + if params[i] != "H0": + list2.append(params[i]) + vals2.append(val) + else: + iH0=i + + # gets the slice corresponding to the best-fit values of all other parameters + # this is 1D, so is our limit on H0 keeping all others fixed + pH0_fixed=ac.get_slice_from_parameters(data,list2,vals2) + + pH0_fixed -= np.max(pH0_fixed) + pH0_fixed = 10**pH0_fixed + pH0_fixed /= np.sum(pH0_fixed) + pH0_fixed /= (uvals[iH0][1]-uvals[iH0][0]) + + # saves this for generating special H0 plot + np.save("ph0_others_all_fixed.npy",[pH0_fixed]) + +main() diff --git a/papers/F/Analysis/Real/make_fig7.py b/papers/F/Analysis/Real/make_fig7.py new file mode 100644 index 00000000..ea1474e9 --- /dev/null +++ b/papers/F/Analysis/Real/make_fig7.py @@ -0,0 +1,186 @@ +""" +This is a script used to produce figures for fig 7 + +It generates two sets of results: +- constraints on alpha (in directory fig8_alphaSingleFigures) +- constraints on other 5 non-H0 parameters (in directory fig_othersSingleFigures) + +Alpha requires special treatment due to the prior not covering +the full range of possible values. +""" + +import numpy as np +import os +import zdm +from zdm import analyze_cube as ac + +from matplotlib import pyplot as plt +import scipy +from IPython import embed + +def main(verbose=False): + + ######### other results #### + Planck_H0 = 67.66 + Planck_sigma = 0.5 + Reiss_H0 = 73.04 + Reiss_sigma = 1.42 + + # output directory + opdir="Figure7/" + if not os.path.exists(opdir): + os.mkdir(opdir) + + CubeFile='Cubes/craco_real_cube.npz' + if os.path.exists(CubeFile): + data=np.load(CubeFile) + else: + print("Could not file cube output file ",CubeFile) + print("Please obtain it from [repository]") + exit() + + # builds uvals list + uvals=[] + latexnames=[] + for ip,param in enumerate(data["params"]): + # switches for alpha + if param=="alpha": + uvals.append(data[param]*-1.) + else: + uvals.append(data[param]) + if param=="alpha": + latexnames.append('$\\alpha$') + ialpha=ip + elif param=="lEmax": + latexnames.append('$\\log_{10} E_{\\rm max}$') + elif param=="H0": + latexnames.append('$H_0$') + elif param=="gamma": + latexnames.append('$\\gamma$') + elif param=="sfr_n": + latexnames.append('$n_{\\rm sfr}$') + elif param=="lmean": + latexnames.append('$\\mu_{\\rm host}$') + elif param=="lsigma": + latexnames.append('$\\sigma_{\\rm host}$') + elif param=="logF": + latexnames.append('$\\log_{10} F$') + + # 1D plots by surveys s only + contributions=[data["lls0"],data["lls2"],data["lls3"],data["lls1"],data["lls4"]] + labels=["CRAFT/FE","CRAFT/ICS 900 MHz","CRAFT/ICS 1.3 GHz","CRAFT/ICS 1.6 GHz","Parkes/Mb"] #correct + + colors=['blue','green','orange','purple','red'] + linestyles=['-',':','--','-','-.'] + make_1d_plots_by_contribution(data,contributions,labels,prefix="Figure7/by_survey_", + colors=colors,linestyles=linestyles)#,latexnames=latexnames) + exit() + +def make_1d_plots_by_contribution( + data, + contributions, + labels, + prefix="", + fig_exten=".png", + log=False, + splines=True, + latexnames=None, + units=None, + linestyles=None, + colors=None, +): + """ + contributions: list of vectors giving various likelihood terms + args: + splines (bool): draw cubic splines + Labels: lists labels stating what these are + latexnames: latex for x and p(X) + units: appends units to x axis but not p(X) + """ + ######################### 1D plots, split by terms ################ + all_uvals = [] + all_vectors = [] + all_wvectors = [] + combined = data["pzDM"] + data["pDM"] + + # gets 1D Bayesian curves for each contribution + for datatype in contributions: + uvals, vectors, wvectors = ac.get_bayesian_data(datatype) + all_uvals.append(uvals) + all_vectors.append(vectors) + all_wvectors.append(wvectors) + params = data["params"] + + # gets unique values for each axis + param_vals = [] + param_list = [ + data["H0"], + data["lmean"], + data["lsigma"], + data["logF"] + ] + xlatexnames = [ + "H_0 {\\rm [km\,s^{-1}\,Mpc^{-1}]}", + "\\mu_{\\rm host} {\\rm [pc\,cm^{-3}]}", + "\\sigma_{\\rm host}", + "\\log_{10} F", + ] + ylatexnames = [ + "H_0", + "\\mu_{\\rm host}", + "\\sigma_{\\rm host}", + "\\log_{10} F", + ] + + for col in param_list: + unique = np.unique(col) + param_vals.append(unique) + # assigns different plotting styles to help distinguish curves + if linestyles is None: + linestyles = ["-", "--", "-.", ":", "-", "--", "-.", ":", "-", "--", "-.", ":"] + if colors is None: + colors = plt.rcParams["axes.prop_cycle"].by_key()["color"] + for which in np.arange(len(param_list)): + plt.figure() + plt.xlabel("$" + xlatexnames[which] + "$") + plt.ylabel("$p(" + ylatexnames[which] + ")$") + xvals = param_vals[which] + + for idata, vectors in enumerate(all_vectors): + # print(which, idata, len(vectors[which]), len(xvals)) + if splines: + xdata = np.linspace(xvals[0], xvals[-1], 100) + f = scipy.interpolate.interp1d( + xvals, np.log(vectors[which]), kind="cubic" + ) + ydata = np.exp(f(xdata)) + plt.plot( + xdata, + ydata, + label=labels[idata], + linestyle=linestyles[idata], + color=colors[idata], + ) + plt.scatter( + xvals, vectors[which], color=plt.gca().lines[-1].get_color() + ) + else: + ydata = vectors[which] + xdata = xvals + # print(labels[idata]," has values ",vector) + plt.plot( + xdata, + ydata, + label=labels[idata], + linestyle=linestyles[idata], + color=colors[idata], + ) + + if log: + plt.yscale("log") + # plt.ylim(np.max(vector)*1e-3,np.max(vector)) #improve this + plt.legend() + plt.savefig(prefix + params[which] + fig_exten, dpi=200) + plt.close() + +main() diff --git a/papers/F/Analysis/Real/py/craco_qck_explore.py b/papers/F/Analysis/Real/py/craco_qck_explore.py new file mode 100644 index 00000000..ed1aa3bb --- /dev/null +++ b/papers/F/Analysis/Real/py/craco_qck_explore.py @@ -0,0 +1,81 @@ +# imports +from importlib import reload +import numpy as np +import sys, os + +from zdm import analyze_cube +from zdm import iteration as it +from zdm import io +from zdm.craco import loading + +from IPython import embed + + +# sys.path.append(os.path.abspath("../../Figures/py")) + + +def main(pargs): + jroot = None + scube = None + if pargs.run == "real": + scube = "real" + outdir = "real/" + + if jroot is None: + jroot = scube + + # Load + npdict = np.load(f"Cubes/craco_{scube}_cube.npz") + + ll_cube = npdict["ll"] + + # Deal with Nan + ll_cube[np.isnan(ll_cube)] = -1e99 + params = npdict["params"] + + # Cube parameters + ############## Load up ############## + pfile = f"Cubes/craco_{jroot}_cube.json" + input_dict = io.process_jfile(pfile) + + # Deconstruct the input_dict + state_dict, cube_dict, vparam_dict = it.parse_input_dict(input_dict) + + # Run Bayes + + # Offset by max + ll_cube = ll_cube - np.max(ll_cube) + + uvals, vectors, wvectors = analyze_cube.get_bayesian_data(ll_cube) + + analyze_cube.do_single_plots( + uvals, vectors, wvectors, params, vparams_dict=vparam_dict, outdir=outdir + ) + print(f"Wrote figures to {outdir}") + + +def parse_option(): + """ + This is a function used to parse the arguments in the training. + + Returns: + args: (dict) dictionary of the arguments. + """ + import argparse + + parser = argparse.ArgumentParser("Slurping the cubes") + parser.add_argument("run", type=str, help="Run to slurp") + # parser.add_argument('--debug', default=False, action='store_true', + # help='Debug?') + args = parser.parse_args() + + return args + + +# Command line execution +if __name__ == "__main__": + + pargs = parse_option() + main(pargs) + +# python py/craco_qck_explore.py mini diff --git a/papers/F/Analysis/Real/py/slurp_craco_cubes.py b/papers/F/Analysis/Real/py/slurp_craco_cubes.py new file mode 100644 index 00000000..1bd99902 --- /dev/null +++ b/papers/F/Analysis/Real/py/slurp_craco_cubes.py @@ -0,0 +1,175 @@ +""" Simple script to slurp """ + +from zdm import analyze_cube + + +def main(pargs): + + if pargs.run == "Emax": + # Emax + input_file = "Cubes/craco_H0_Emax_cube.json" + prefix = "Cubes/tmp" + nsurveys = 1 + + # Run it + analyze_cube.slurp_cube( + input_file, prefix, "Cubes/craco_H0_Emax_cube.npz", nsurveys + ) + + elif pargs.run == "F": + # Emax + input_file = "Cubes/craco_H0_F_cube.json" + prefix = "Cloud/Output/craco_H0_F" + nsurveys = 1 + + # Run it + analyze_cube.slurp_cube( + input_file, prefix, "Cubes/craco_H0_F_cube.npz", nsurveys + ) + + elif pargs.run == "logF": + # Emax + input_file = "Cubes/craco_H0_logF_cube.json" + prefix = "Cloud/Output_logF_test/craco_H0_logF" + nsurveys = 1 + + # Run it + analyze_cube.slurp_cube( + input_file, prefix, "Cubes/craco_H0_logF_cube.npz", nsurveys + ) + + elif pargs.run == "logF_full": + # Emax + input_file = "Cubes/craco_full_cube.json" + prefix = "Cloud/OutputFull/craco_full" + nsurveys = 1 + + # Run it + analyze_cube.slurp_cube( + input_file, prefix, "Cubes/craco_full_cube.npz", nsurveys + ) + + elif pargs.run == "lmF": + # Emax + input_file = "Cubes/craco_lm_F_cube.json" + prefix = "Cloud/Output/craco_lm_F" + nsurveys = 1 + + # Run it + analyze_cube.slurp_cube( + input_file, prefix, "Cubes/craco_lm_F_cube.npz", nsurveys + ) + + elif pargs.run == "mini": + # Emax + input_file = "Cubes/craco_mini_cube.json" + # prefix = 'Cubes/craco_mini' + prefix = "Cloud/OutputMini/craco_mini" + nsurveys = 1 + + # Run it + analyze_cube.slurp_cube( + input_file, prefix, "Cubes/craco_mini_cube.npz", nsurveys + ) + elif pargs.run == "submini": + # Emax + input_file = "Cubes/craco_submini_cube.json" + prefix = "Cubes/craco_submini_cube" + nsurveys = 1 + + # Run it + analyze_cube.slurp_cube( + input_file, prefix, "Cubes/craco_submini_cube.npz", nsurveys + ) + + elif pargs.run == "sfrEmax": + # Emax + input_file = "Cubes/craco_sfr_Emax_cube.json" + prefix = "Cubes/craco_sfr_Emax_cube" + nsurveys = 1 + + # Run it + analyze_cube.slurp_cube( + input_file, prefix, "Cubes/craco_sfr_Emax_cube.npz", nsurveys + ) + + elif pargs.run == "alphaEmax": + # Emax + input_file = "Cubes/craco_alpha_Emax_cube.json" + prefix = "Cubes/craco_alpha_Emax_cube" + nsurveys = 1 + + # Run it + analyze_cube.slurp_cube( + input_file, prefix, "Cubes/craco_alpha_Emax_cube.npz", nsurveys + ) + elif pargs.run == "full": + # Emax + input_file = "Cubes/craco_full_cube.json" + prefix = "Cubes/craco_full" + nsurveys = 1 + + # Run it + analyze_cube.slurp_cube( + input_file, prefix, "Cubes/craco_full_cube.npz", nsurveys + ) + elif pargs.run == "another_full": + # Emax + input_file = "Cubes/craco_full_cube.json" + prefix = "Cubes/craco_400_full" + nsurveys = 1 + + # Run it + analyze_cube.slurp_cube( + input_file, prefix, "Cubes/craco_400_full_cube.npz", nsurveys + ) + elif pargs.run == "third_full": + # Emax + input_file = "Cubes/craco_full_cube.json" + prefix = "Cubes/craco_3rd_full" + nsurveys = 1 + + # Run it + analyze_cube.slurp_cube( + input_file, prefix, "Cubes/craco_3rd_full_cube.npz", nsurveys + ) + elif pargs.run == "real": + # Emax + input_file = "Cubes/craco_real_cube.json" + prefix = "Cloud/Output/craco_real" + nsurveys = 1 + + # Run it + analyze_cube.slurp_cube( + input_file, prefix, "Cubes/craco_real_cube.npz", nsurveys + ) + + +def parse_option(): + """ + This is a function used to parse the arguments in the training. + + Returns: + args: (dict) dictionary of the arguments. + """ + import argparse + + parser = argparse.ArgumentParser("Slurping the cubes") + parser.add_argument("run", type=str, help="Run to slurp") + # parser.add_argument('--debug', default=False, action='store_true', + # help='Debug?') + args = parser.parse_args() + + return args + + +# Command line execution +if __name__ == "__main__": + + pargs = parse_option() + main(pargs) + +# python py/slurp_craco_cubes.py mini +# python py/slurp_craco_cubes.py another_full + +# python py/slurp_craco_cubes.py F diff --git a/papers/F/Analysis/Real/testF.py b/papers/F/Analysis/Real/testF.py new file mode 100644 index 00000000..b253d0e1 --- /dev/null +++ b/papers/F/Analysis/Real/testF.py @@ -0,0 +1,138 @@ +import numpy as np +import os +import zdm +from zdm import analyze_cube as ac + +from matplotlib import pyplot as plt +from IPython import embed + +def main(verbose=False): + + # output directory + opdir="2d_figs/" + if not os.path.exists(opdir): + os.mkdir(opdir) + + CubeFile='Cubes/craco_real_cube.npz' + if os.path.exists(CubeFile): + data=np.load(CubeFile) + else: + print("Could not file cube output file ",CubeFile) + print("Please obtain it from [repository]") + exit() + + if verbose: + print("Data file contains the following items") + for thing in data: + print(thing) + + lst = data.files + lldata=data["ll"] + params=data["params"] + + def get_param_values(data,params): + """ + Gets the unique values of the data from a cube output + Currently the parameter order is hard-coded + + """ + param_vals=[] + for param in params: + col=data[param] + unique=np.unique(col) + param_vals.append(unique) + return param_vals + + param_vals=get_param_values(data, params) + + # builds uvals list + uvals=[] + latexnames=[] + for ip,param in enumerate(data["params"]): + # switches for alpha + if param=="alpha": + uvals.append(data[param]*-1.) + else: + uvals.append(data[param]) + if param=="alpha": + latexnames.append('$\\alpha$') + ialpha=ip + elif param=="lEmax": + latexnames.append('$\\log_{10} E_{\\rm max}$') + elif param=="H0": + latexnames.append('$H_0$') + elif param=="gamma": + latexnames.append('$\\gamma$') + elif param=="sfr_n": + latexnames.append('$n_{\\rm sfr}$') + elif param=="lmean": + latexnames.append('$\\mu_{\\rm host}$') + elif param=="lsigma": + latexnames.append('$\\sigma_{\\rm host}$') + elif param=="logF": + latexnames.append('$\\log_{10} F$') + + #latexnames=['$\\log_{10} E_{\\rm max}$','$H_0$','$\\alpha$','$\\gamma$','$n_{\\rm sfr}$','$\\mu_{\\rm host}$','$\\sigma_{\\rm host}$'] + + list2=[] + vals2=[] + # gets Bayesian posteriors + deprecated,uw_vectors,wvectors=ac.get_bayesian_data(data["ll"]) + for i,vec in enumerate(uw_vectors): + n=np.argmax(vec) + val=uvals[i][n] + if params[i] != "logF": + list2.append(params[i]) + vals2.append(val) + else: + iF=i + + ###### NOTATION ##### + # uw: unweighted + # wH0: weighted according to H0 knowledged + # f: fixed other parameters + # B: best-fit + + ############## 2D plots at best-fit valuess ########## + + # gets the slice corresponding to the best-fit values of all other parameters + # this is 1D, so is our limit on H0 keeping all others fixed + for i,item in enumerate(list2): + + list3=np.concatenate((list2[0:i],list2[i+1:])) + vals3=np.concatenate((vals2[0:i],vals2[i+1:])) + array=ac.get_slice_from_parameters(data,list3,vals3) + + # log to lin space + array[np.isnan(array)] = -1e99 + array -= np.max(array) + array = 10**array + array /= np.sum(array) + + # now have array for slice covering best-fit values + if i < iF: + modi=i + else: + modi=i+1 + #array=array.T + array=array.swapaxes(0,1) + savename=opdir+"/lls_"+params[iF]+"_"+params[modi]+".png" + +# if (latexnames[modi] == '$\\gamma$'): +# embed(header="gamma") + +# if (latexnames[modi] == '$H_0$'): +# embed(header="H0") + + if params[modi]=="alpha": + #switches order of array in alpha dimension + array=np.flip(array,axis=0) + ac.make_2d_plot(array,latexnames[modi],latexnames[iF], + -param_vals[modi],param_vals[iF], + savename=savename,norm=1) + else: + ac.make_2d_plot(array,latexnames[modi],latexnames[iF], + param_vals[modi],param_vals[iF], + savename=savename,norm=1) + +main() \ No newline at end of file diff --git a/papers/F/Analysis/Real/tmp.ipynb b/papers/F/Analysis/Real/tmp.ipynb new file mode 100644 index 00000000..de94906f --- /dev/null +++ b/papers/F/Analysis/Real/tmp.ipynb @@ -0,0 +1,150 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import zdm.analyze_cube as ac\n", + "import matplotlib.pyplot as plt\n", + "import zdm.analyze_cube as ac" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "cube_dir = \"./Cubes/craco_real_cube.npz\"\n", + "\n", + "cube=np.load(cube_dir)\n", + "ll = ac.get_slice_from_parameters(cube, [\"H0\"], [74], wanted=\"ll\")\n", + "_, vectors, _= ac.get_bayesian_data(ll)\n", + "plt.plot(cube[\"logF\"], vectors[-1])" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.0" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.max(ll.flatten())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "cube_dir = \"../CRACO/Cubes/craco_full_cube.npz\"\n", + "cube=np.load(cube_dir)\n", + "ll = ac.get_slice_from_parameters(cube, [\"H0\"], [74], wanted=\"ll\")\n", + "_, vectors, _= ac.get_bayesian_data(ll)\n", + "plt.plot(cube[\"logF\"], vectors[-1])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.8.5 ('base')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5 (default, Sep 4 2020, 02:22:02) \n[Clang 10.0.0 ]" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "9731a3b7ebb41545a410367c249afbc4842fd5740da82f1bd29e6ac1d7253e6e" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/papers/F/Analysis/py/analy_F_I.py b/papers/F/Analysis/py/analy_F_I.py index 361282d8..f94c129e 100644 --- a/papers/F/Analysis/py/analy_F_I.py +++ b/papers/F/Analysis/py/analy_F_I.py @@ -1,6 +1,6 @@ from zdm.craco import loading -fiducial_survey = "CRACO_F_0.32_survey" +fiducial_survey = "../MC_F/Surveys/F_0.32_survey" def craco_mc_survey_grid(iFRB=100): diff --git a/papers/F/Figures/py/figs_compare.py b/papers/F/Figures/py/figs_compare.py new file mode 100644 index 00000000..675d85b6 --- /dev/null +++ b/papers/F/Figures/py/figs_compare.py @@ -0,0 +1,160 @@ +import os, sys +import numpy as np + +from matplotlib import pyplot as plt +from scipy.interpolate import interp1d + +from frb.dm import igm as figm +from frb.figures import utils as fig_utils + +from zdm import figures, pcosmic + +sys.path.append(os.path.abspath("../Analysis/py")) +sys.path.append(os.path.abspath("../../Analysis/py")) +import analy_F_I + +from astropy.cosmology import FlatLambdaCDM + + +def fig_varyF( + outfile, + Fs, + H0s, + lmeans, + lsigmas, + zmax=2.3, + DMmax=1500, + Aconts=[0.05], + lcolors=["b"], + lstyles=["-"], + labels=[""], + zticks=None, + ylim=None, + iFRB=0, + show_FRBs=True, +): + + survey, grid = analy_F_I.craco_mc_survey_grid(iFRB=iFRB) + + fiducial_F = grid.state.IGM.logF + fiducial_H0 = grid.state.cosmo.H0 + fiducial_lmean = grid.state.host.lmean + fiducial_lsigma = grid.state.host.lsigma + + fig, ax = plt.subplots(dpi=200) + + ax.set_xlabel("z") + ax.set_ylabel("${\\rm DM}_{\\rm EG}$") + + legend_lines = [] + + for F, H0, lmean, lsigma, lstyle, color in zip( + Fs, H0s, lmeans, lsigmas, lstyles, lcolors + ): + + vparams = {} + + if F is None: + F = fiducial_F + if H0 is None: + H0 = fiducial_H0 + if lmean is None: + lmean = fiducial_lmean + if lsigma is None: + lsigma = fiducial_lsigma + + vparams["logF"] = F + vparams["H0"] = H0 + vparams["lmean"] = lmean + vparams["lsigma"] = lsigma + + grid.update(vparams) + + full_zDMgrid, zvals, dmvals = ( + grid.rates.copy(), + grid.zvals.copy(), + grid.dmvals.copy(), + ) + + zvals, dmvals, zDMgrid = figures.proc_pgrid( + full_zDMgrid, zvals, (0, zmax), dmvals, (0, DMmax) + ) + + alevels = figures.find_Alevels(full_zDMgrid, Aconts) + + plt.sca(ax) + + tvals, ticks = figures.ticks_pgrid(zvals, these_vals=zticks) + plt.xticks(tvals, ticks) + + tvals, ticks = figures.ticks_pgrid(dmvals, fmt="int") + plt.yticks(tvals, ticks) + + cs = ax.contour( + zDMgrid.T, levels=alevels, origin="lower", colors=[color], linestyles=lstyle + ) + + leg, _ = cs.legend_elements() + legend_lines.append(leg[0]) + + ### TEST + # Interpolators + f_DM = interp1d( + dmvals, np.arange(dmvals.size), fill_value="extrapolate", bounds_error=False + ) + f_z = interp1d( + zvals, np.arange(zvals.size), fill_value="extrapolate", bounds_error=False + ) + + cosmo = FlatLambdaCDM( + H0=grid.state.cosmo.H0, + Ob0=grid.state.cosmo.Omega_b, + Om0=grid.state.cosmo.Omega_m, + ) + + dms, zeval = figm.average_DM(3.0, cumul=True, cosmo=cosmo) + + l_mqr = ax.plot(f_z(zeval), f_DM(dms), ls="--", c=color, alpha=0.5) + + # put down FRBs + FRBZ = survey.frbs["Z"] + FRBDM = survey.DMEGs + + ddm = dmvals[1] - dmvals[0] + dz = zvals[1] - zvals[0] + nz, ndm = zDMgrid.shape + + ##### add FRB host galaxies at some DM/redshift ##### + if (FRBZ is not None) and show_FRBs: + iDMs = FRBDM / ddm + iZ = FRBZ / dz + # Restrict to plot range + gd = (FRBDM < DMmax) & (FRBZ < zmax) + ax.plot(iZ[gd], iDMs[gd], "ko", linestyle="", markersize=2.0) + + ax.legend(legend_lines, labels, loc="lower right") + + # Fontsize + fig_utils.set_fontsize(ax, 16.0) + + fig.tight_layout() + plt.savefig(outfile, dpi=300, bbox_inches="tight") + plt.close() + print(f"Wrote: {outfile}") + + +fig_varyF( + "fig_varyF_H0_compare.png", + Fs=[-0.57, -0.37, None], + H0s=[69.02, 77.14, None], + lmeans=[2.21, 2.33, None], + lsigmas=[0.52, 0.53, None], + lcolors=["r", "b", "k"], + lstyles=["-", "-", "--"], + labels=["Synthetic", "Real", "Fiducial"], + DMmax=2500, + Aconts=[0.01], + show_FRBs=False, + zmax=3, +) + diff --git a/papers/F/Figures/py/figs_zdm_F_I.py b/papers/F/Figures/py/figs_zdm_F_I.py index e4d13e65..f0e1ee9f 100644 --- a/papers/F/Figures/py/figs_zdm_F_I.py +++ b/papers/F/Figures/py/figs_zdm_F_I.py @@ -26,6 +26,7 @@ def fig_craco_varyF_zDM( fuss_with_ticks: bool = False, suppress_DM_host=False, iFRB=0, + show_FRBS=True ): """_summary_ @@ -138,9 +139,9 @@ def fig_craco_varyF_zDM( + f"= {vparams['lEmax']}" ) elif other_param == "H0": - labels.append(r"$\\log_\{10\} F = " + f"{F}, H0 = {vparams['H0']}") + labels.append(r"$\log_\{10\} F = " + f"{F}, H0 = {vparams['H0']}") elif other_param == "lmean": - labels.append(r"$\\log_\{10\} F = " + f"{F}, $\mu =$ {vparams['lmean']}") + labels.append(r"$\log_\{10\} F = " + f"{F}, $\mu =$ {vparams['lmean']}") ###### gets decent axis labels, down to 1 decimal place ####### ax = plt.gca() @@ -181,7 +182,7 @@ def fig_craco_varyF_zDM( nz, ndm = zDMgrid.shape ##### add FRB host galaxies at some DM/redshift ##### - if FRBZ is not None: + if (FRBZ is not None) and show_FRBS: iDMs = FRBDM / ddm iZ = FRBZ / dz # Restrict to plot range @@ -227,6 +228,7 @@ def fig_varyF( zticks=None, ylim=None, iFRB=0, + show_FRBs=True ): survey, grid = analy_F_I.craco_mc_survey_grid(iFRB=iFRB) @@ -327,9 +329,9 @@ def fig_varyF( + f"= {vparams['lEmax']}" ) elif other_param == "H0": - labels.append(r"$\\log_\{10\} F = $" + f"{F}, H0 = {vparams['H0']}") + labels.append(r"$\log_{10} F = $" + f"{F}, H0 = {vparams['H0']}") elif other_param == "lmean": - labels.append(r"$\\log_\{10\} F = $" + f"{F}, $\mu =$ {vparams['lmean']}") + labels.append(r"$\log_{10} F = $" + f"{F}, $\mu =$ {vparams['lmean']}") # # Interpolators # f_DM = interp1d( @@ -362,7 +364,7 @@ def fig_varyF( nz, ndm = zDMgrid.shape ##### add FRB host galaxies at some DM/redshift ##### - if FRBZ is not None: + if (FRBZ is not None) and show_FRBs: iDMs = FRBDM / ddm iZ = FRBZ / dz # Restrict to plot range @@ -401,6 +403,7 @@ def fig_craco_fiducial_F( H0=None, iFRB=0, suppress_DM_host=False, + show_FRBs=True ): """ Very complicated routine for plotting 2D zdm grids @@ -547,7 +550,7 @@ def fig_craco_fiducial_F( plt.clim(themin, themax) ##### add FRB host galaxies at some DM/redshift ##### - if FRBZ is not None: + if (FRBZ is not None) and show_FRBs: iDMs = FRBDM / ddm iZ = FRBZ / dz # Restrict to plot range @@ -568,7 +571,6 @@ def fig_craco_fiducial_F( print(f"Wrote: {outfile}") plt.close() - ### tests # logfs = [-1.5, -1.5, -1.5] @@ -585,22 +587,34 @@ def fig_craco_fiducial_F( # ) # fig_varyF( -# "fig_varyF_H0_60.png", +# "fig_varyF_H0_compare.png", # other_param="H0", -# F_values=[-1.7, -1.2, -0.8], -# other_values=[60.0, 60.0, 60.0], -# lcolors=["#f72585", "#f8961e", "#4895ef"], -# lstyles=["-", "-", "-"], -# DMmax=1800, +# F_values=[-0.57, -.37], +# other_values=[69.02, 77.14], +# lcolors=["r", "b"], +# lstyles=["-", "-"], +# DMmax=2500, # Aconts=[0.01], +# show_FRBs=False, +# zmax=3 # ) fig_craco_fiducial_F( - f"figs/fiducial.png", + f"figs/high_feedback_efficiency.png", show_Macquart=True, - F=np.round(np.log10(0.32), 3), + F=np.round(np.log10(0.01), 3), H0=None, suppress_DM_host=False, iFRB=100, + show_FRBs=False ) +fig_craco_fiducial_F( + f"figs/low_feedback_efficiency.png", + show_Macquart=True, + F=np.round(np.log10(0.9), 3), + H0=None, + suppress_DM_host=False, + iFRB=100, + show_FRBs=False +) diff --git a/zdm/analyze_cube.py b/zdm/analyze_cube.py index 968eaf6d..195eeb46 100644 --- a/zdm/analyze_cube.py +++ b/zdm/analyze_cube.py @@ -1164,6 +1164,8 @@ def do_single_plots(uvals,vectors,wvectors,names,tag=None, fig_exten='.png', ) ) other_styles = [":", "--", "-."] + + # other_colors = ["orange", "green", "red"] # plot any other plots if others is not None: if others[i] is not None: @@ -1172,6 +1174,11 @@ def do_single_plots(uvals,vectors,wvectors,names,tag=None, fig_exten='.png', norm = np.sum(y) * (x[1] - x[0]) # integral y dx ~ sum y delta x norm = np.abs(norm) y /= norm + + # data_norm = np.sum(data) * (vals[1] - vals[0]) + # data_norm = np.abs(norm) + + # plt.plot(vals, data/data_norm, color=other_colors[io % 3], marker="s") plt.plot( x, y, color="grey", linewidth=1, linestyle=other_styles[io % 3] ) @@ -1203,7 +1210,7 @@ def do_single_plots(uvals,vectors,wvectors,names,tag=None, fig_exten='.png', plt.legend(loc="upper left", title="Prior on $\\alpha$") plt.tight_layout() - plt.savefig(os.path.join(outdir, prefix + names[i] + fig_exten), dpi=200) + plt.savefig(os.path.join(outdir, prefix + names[i] + fig_exten), dpi=300) plt.close() if log: logfile.close() diff --git a/zdm/craco/MC_F/Surveys/F_0.32_survey.ecsv b/zdm/craco/MC_F/Surveys/F_0.32_survey.ecsv new file mode 100644 index 00000000..bcc05a2a --- /dev/null +++ b/zdm/craco/MC_F/Surveys/F_0.32_survey.ecsv @@ -0,0 +1,1024 @@ +# %ECSV 0.9 +# --- +# datatype: +# - {name: TNS, datatype: string} +# - {name: BW, datatype: float64} +# - {name: DM, datatype: float64} +# - {name: DMG, datatype: float64} +# - {name: FBAR, datatype: float64} +# - {name: FRES, datatype: float64} +# - {name: Gb, datatype: object} +# - {name: Gl, datatype: object} +# - {name: SNR, datatype: float64} +# - {name: SNRTHRESH, datatype: float64} +# - {name: THRESH, datatype: float64} +# - {name: TRES, datatype: float64} +# - {name: WIDTH, datatype: float64} +# - {name: XDec, datatype: object} +# - {name: XRA, datatype: object} +# - {name: Z, datatype: float64} +# meta: !!omap +# - {survey_data: "{\n \"observing\": {\n \"NORM_FRB\": 1000,\n \"TOBS\": 96.65\n },\n \"telescope\": {\n \ +# \ \"BEAM\": \"lat50_log\",\n \"DIAM\": 12.0,\n \"NBEAMS\": 36\n }\n}"} +# schema: astropy-2.0 +TNS BW DM DMG FBAR FRES Gb Gl SNR SNRTHRESH THRESH TRES WIDTH XDec XRA Z +0 288.0 550.1 35.0 1320.0 1.0 "" "" 25.5 9.5 0.99 1.7 2.0 "" "" 0.313 +1 288.0 273.7 35.0 1320.0 1.0 "" "" 22.8 9.5 0.99 1.7 2.0 "" "" 0.02 +2 288.0 680.2 35.0 1320.0 1.0 "" "" 10.2 9.5 0.99 1.7 1.0 "" "" 0.645 +3 288.0 515.1 35.0 1320.0 1.0 "" "" 36.1 9.5 0.99 1.7 3.0 "" "" 0.179 +4 288.0 572.3 35.0 1320.0 1.0 "" "" 44.0 9.5 0.99 1.7 5.0 "" "" 0.207 +5 288.0 330.9 35.0 1320.0 1.0 "" "" 17.6 9.5 0.99 1.7 4.0 "" "" 0.299 +6 288.0 888.9 35.0 1320.0 1.0 "" "" 22.3 9.5 0.99 1.7 3.0 "" "" 0.737 +7 288.0 378.2 35.0 1320.0 1.0 "" "" 25.7 9.5 0.99 1.7 2.0 "" "" 0.358 +8 288.0 458.5 35.0 1320.0 1.0 "" "" 104.3 9.5 0.99 1.7 1.0 "" "" 0.047 +9 288.0 1047.4 35.0 1320.0 1.0 "" "" 19.8 9.5 0.99 1.7 2.0 "" "" 1.259 +10 288.0 349.8 35.0 1320.0 1.0 "" "" 39.3 9.5 0.99 1.7 4.0 "" "" 0.3 +11 288.0 376.2 35.0 1320.0 1.0 "" "" 15.3 9.5 0.99 1.7 2.0 "" "" 0.476 +12 288.0 240.3 35.0 1320.0 1.0 "" "" 33.6 9.5 0.99 1.7 1.0 "" "" 0.254 +13 288.0 495.1 35.0 1320.0 1.0 "" "" 13.1 9.5 0.99 1.7 3.0 "" "" 0.486 +14 288.0 302.0 35.0 1320.0 1.0 "" "" 16.2 9.5 0.99 1.7 3.0 "" "" 0.061 +15 288.0 465.4 35.0 1320.0 1.0 "" "" 46.8 9.5 0.99 1.7 3.0 "" "" 0.222 +16 288.0 298.9 35.0 1320.0 1.0 "" "" 12.0 9.5 0.99 1.7 3.0 "" "" 0.173 +17 288.0 362.7 35.0 1320.0 1.0 "" "" 12.0 9.5 0.99 1.7 4.0 "" "" 0.074 +18 288.0 1707.5 35.0 1320.0 1.0 "" "" 16.8 9.5 0.99 1.7 3.0 "" "" 0.842 +19 288.0 122.8 35.0 1320.0 1.0 "" "" 53.0 9.5 0.99 1.7 2.0 "" "" 0.003 +20 288.0 405.5 35.0 1320.0 1.0 "" "" 58.0 9.5 0.99 1.7 3.0 "" "" 0.538 +21 288.0 896.8 35.0 1320.0 1.0 "" "" 13.1 9.5 0.99 1.7 3.0 "" "" 0.754 +22 288.0 300.8 35.0 1320.0 1.0 "" "" 19.6 9.5 0.99 1.7 2.0 "" "" 0.163 +23 288.0 913.3 35.0 1320.0 1.0 "" "" 10.6 9.5 0.99 1.7 2.0 "" "" 0.768 +24 288.0 763.0 35.0 1320.0 1.0 "" "" 31.8 9.5 0.99 1.7 3.0 "" "" 0.716 +25 288.0 1107.1 35.0 1320.0 1.0 "" "" 13.2 9.5 0.99 1.7 2.0 "" "" 1.02 +26 288.0 417.6 35.0 1320.0 1.0 "" "" 138.9 9.5 0.99 1.7 1.0 "" "" 0.352 +27 288.0 355.0 35.0 1320.0 1.0 "" "" 14.8 9.5 0.99 1.7 2.0 "" "" 0.283 +28 288.0 240.7 35.0 1320.0 1.0 "" "" 18.7 9.5 0.99 1.7 3.0 "" "" 0.038 +29 288.0 618.0 35.0 1320.0 1.0 "" "" 37.9 9.5 0.99 1.7 4.0 "" "" 0.318 +30 288.0 975.5 35.0 1320.0 1.0 "" "" 17.5 9.5 0.99 1.7 2.0 "" "" 0.407 +31 288.0 403.9 35.0 1320.0 1.0 "" "" 10.0 9.5 0.99 1.7 3.0 "" "" 0.124 +32 288.0 975.2 35.0 1320.0 1.0 "" "" 11.3 9.5 0.99 1.7 2.0 "" "" 1.093 +33 288.0 142.8 35.0 1320.0 1.0 "" "" 20.2 9.5 0.99 1.7 4.0 "" "" 0.001 +34 288.0 299.6 35.0 1320.0 1.0 "" "" 16.5 9.5 0.99 1.7 1.0 "" "" 0.208 +35 288.0 243.5 35.0 1320.0 1.0 "" "" 10.1 9.5 0.99 1.7 0.0 "" "" 0.05 +36 288.0 1455.2 35.0 1320.0 1.0 "" "" 10.6 9.5 0.99 1.7 2.0 "" "" 1.873 +37 288.0 178.4 35.0 1320.0 1.0 "" "" 68.0 9.5 0.99 1.7 2.0 "" "" 0.082 +38 288.0 719.1 35.0 1320.0 1.0 "" "" 10.1 9.5 0.99 1.7 4.0 "" "" 0.927 +39 288.0 795.7 35.0 1320.0 1.0 "" "" 11.8 9.5 0.99 1.7 1.0 "" "" 0.495 +40 288.0 397.4 35.0 1320.0 1.0 "" "" 10.4 9.5 0.99 1.7 2.0 "" "" 0.347 +41 288.0 350.3 35.0 1320.0 1.0 "" "" 18.2 9.5 0.99 1.7 2.0 "" "" 0.111 +42 288.0 218.6 35.0 1320.0 1.0 "" "" 12.9 9.5 0.99 1.7 1.0 "" "" 0.092 +43 288.0 332.3 35.0 1320.0 1.0 "" "" 29.6 9.5 0.99 1.7 3.0 "" "" 0.179 +44 288.0 435.0 35.0 1320.0 1.0 "" "" 16.5 9.5 0.99 1.7 3.0 "" "" 0.256 +45 288.0 568.2 35.0 1320.0 1.0 "" "" 11.9 9.5 0.99 1.7 1.0 "" "" 0.463 +46 288.0 1062.3 35.0 1320.0 1.0 "" "" 18.1 9.5 0.99 1.7 1.0 "" "" 1.071 +47 288.0 401.7 35.0 1320.0 1.0 "" "" 30.1 9.5 0.99 1.7 2.0 "" "" 0.293 +48 288.0 589.2 35.0 1320.0 1.0 "" "" 13.1 9.5 0.99 1.7 1.0 "" "" 0.503 +49 288.0 244.4 35.0 1320.0 1.0 "" "" 36.6 9.5 0.99 1.7 3.0 "" "" 0.014 +50 288.0 223.5 35.0 1320.0 1.0 "" "" 193.2 9.5 0.99 1.7 3.0 "" "" 0.224 +51 288.0 1308.0 35.0 1320.0 1.0 "" "" 10.3 9.5 0.99 1.7 2.0 "" "" 0.453 +52 288.0 384.5 35.0 1320.0 1.0 "" "" 23.9 9.5 0.99 1.7 1.0 "" "" 0.286 +53 288.0 678.3 35.0 1320.0 1.0 "" "" 10.5 9.5 0.99 1.7 3.0 "" "" 0.588 +54 288.0 488.8 35.0 1320.0 1.0 "" "" 9.8 9.5 0.99 1.7 1.0 "" "" 0.333 +55 288.0 418.4 35.0 1320.0 1.0 "" "" 12.0 9.5 0.99 1.7 2.0 "" "" 0.382 +56 288.0 388.3 35.0 1320.0 1.0 "" "" 55.5 9.5 0.99 1.7 4.0 "" "" 0.354 +57 288.0 479.7 35.0 1320.0 1.0 "" "" 55.6 9.5 0.99 1.7 2.0 "" "" 0.135 +58 288.0 176.3 35.0 1320.0 1.0 "" "" 40.5 9.5 0.99 1.7 2.0 "" "" 0.078 +59 288.0 328.3 35.0 1320.0 1.0 "" "" 16.8 9.5 0.99 1.7 1.0 "" "" 0.277 +60 288.0 577.7 35.0 1320.0 1.0 "" "" 38.5 9.5 0.99 1.7 2.0 "" "" 0.683 +61 288.0 509.6 35.0 1320.0 1.0 "" "" 11.7 9.5 0.99 1.7 0.0 "" "" 0.359 +62 288.0 398.7 35.0 1320.0 1.0 "" "" 18.9 9.5 0.99 1.7 2.0 "" "" 0.483 +63 288.0 691.3 35.0 1320.0 1.0 "" "" 20.8 9.5 0.99 1.7 2.0 "" "" 0.197 +64 288.0 493.2 35.0 1320.0 1.0 "" "" 15.2 9.5 0.99 1.7 3.0 "" "" 0.374 +65 288.0 350.9 35.0 1320.0 1.0 "" "" 93.5 9.5 0.99 1.7 3.0 "" "" 0.028 +66 288.0 428.1 35.0 1320.0 1.0 "" "" 16.8 9.5 0.99 1.7 4.0 "" "" 0.435 +67 288.0 801.0 35.0 1320.0 1.0 "" "" 16.2 9.5 0.99 1.7 4.0 "" "" 1.053 +68 288.0 412.7 35.0 1320.0 1.0 "" "" 15.2 9.5 0.99 1.7 2.0 "" "" 0.172 +69 288.0 907.4 35.0 1320.0 1.0 "" "" 26.8 9.5 0.99 1.7 1.0 "" "" 0.497 +70 288.0 461.0 35.0 1320.0 1.0 "" "" 125.7 9.5 0.99 1.7 3.0 "" "" 0.272 +71 288.0 943.3 35.0 1320.0 1.0 "" "" 16.8 9.5 0.99 1.7 3.0 "" "" 0.638 +72 288.0 419.5 35.0 1320.0 1.0 "" "" 59.2 9.5 0.99 1.7 1.0 "" "" 0.234 +73 288.0 187.7 35.0 1320.0 1.0 "" "" 168.5 9.5 0.99 1.7 2.0 "" "" 0.064 +74 288.0 551.6 35.0 1320.0 1.0 "" "" 26.4 9.5 0.99 1.7 3.0 "" "" 0.459 +75 288.0 276.6 35.0 1320.0 1.0 "" "" 25.6 9.5 0.99 1.7 0.0 "" "" 0.288 +76 288.0 275.7 35.0 1320.0 1.0 "" "" 28.0 9.5 0.99 1.7 3.0 "" "" 0.141 +77 288.0 421.4 35.0 1320.0 1.0 "" "" 13.7 9.5 0.99 1.7 1.0 "" "" 0.097 +78 288.0 345.1 35.0 1320.0 1.0 "" "" 11.3 9.5 0.99 1.7 1.0 "" "" 0.222 +79 288.0 476.7 35.0 1320.0 1.0 "" "" 15.6 9.5 0.99 1.7 3.0 "" "" 0.191 +80 288.0 277.7 35.0 1320.0 1.0 "" "" 645.9 9.5 0.99 1.7 1.0 "" "" 0.055 +81 288.0 411.0 35.0 1320.0 1.0 "" "" 12.3 9.5 0.99 1.7 2.0 "" "" 0.333 +82 288.0 654.8 35.0 1320.0 1.0 "" "" 16.8 9.5 0.99 1.7 4.0 "" "" 0.06 +83 288.0 440.5 35.0 1320.0 1.0 "" "" 20.4 9.5 0.99 1.7 0.0 "" "" 0.101 +84 288.0 355.8 35.0 1320.0 1.0 "" "" 89.8 9.5 0.99 1.7 3.0 "" "" 0.184 +85 288.0 627.5 35.0 1320.0 1.0 "" "" 13.5 9.5 0.99 1.7 5.0 "" "" 0.428 +86 288.0 1497.3 35.0 1320.0 1.0 "" "" 10.1 9.5 0.99 1.7 3.0 "" "" 1.597 +87 288.0 220.6 35.0 1320.0 1.0 "" "" 16.7 9.5 0.99 1.7 3.0 "" "" 0.127 +88 288.0 219.8 35.0 1320.0 1.0 "" "" 10.2 9.5 0.99 1.7 1.0 "" "" 0.104 +89 288.0 193.1 35.0 1320.0 1.0 "" "" 15.0 9.5 0.99 1.7 3.0 "" "" 0.127 +90 288.0 532.5 35.0 1320.0 1.0 "" "" 13.4 9.5 0.99 1.7 2.0 "" "" 0.49 +91 288.0 1473.7 35.0 1320.0 1.0 "" "" 12.6 9.5 0.99 1.7 3.0 "" "" 0.689 +92 288.0 528.4 35.0 1320.0 1.0 "" "" 9.6 9.5 0.99 1.7 2.0 "" "" 0.256 +93 288.0 886.6 35.0 1320.0 1.0 "" "" 10.3 9.5 0.99 1.7 1.0 "" "" 0.942 +94 288.0 558.4 35.0 1320.0 1.0 "" "" 39.2 9.5 0.99 1.7 2.0 "" "" 0.827 +95 288.0 751.5 35.0 1320.0 1.0 "" "" 19.6 9.5 0.99 1.7 2.0 "" "" 0.788 +96 288.0 365.8 35.0 1320.0 1.0 "" "" 48.4 9.5 0.99 1.7 3.0 "" "" 0.071 +97 288.0 442.8 35.0 1320.0 1.0 "" "" 10.4 9.5 0.99 1.7 2.0 "" "" 0.29 +98 288.0 808.5 35.0 1320.0 1.0 "" "" 11.3 9.5 0.99 1.7 2.0 "" "" 0.421 +99 288.0 1026.1 35.0 1320.0 1.0 "" "" 14.3 9.5 0.99 1.7 4.0 "" "" 1.304 +100 288.0 186.7 35.0 1320.0 1.0 "" "" 15.9 9.5 0.99 1.7 2.0 "" "" 0.15 +101 288.0 1179.5 35.0 1320.0 1.0 "" "" 26.0 9.5 0.99 1.7 1.0 "" "" 1.321 +102 288.0 438.5 35.0 1320.0 1.0 "" "" 20.2 9.5 0.99 1.7 4.0 "" "" 0.062 +103 288.0 315.3 35.0 1320.0 1.0 "" "" 17.9 9.5 0.99 1.7 1.0 "" "" 0.089 +104 288.0 833.1 35.0 1320.0 1.0 "" "" 10.5 9.5 0.99 1.7 2.0 "" "" 0.949 +105 288.0 595.3 35.0 1320.0 1.0 "" "" 32.8 9.5 0.99 1.7 2.0 "" "" 0.25 +106 288.0 313.4 35.0 1320.0 1.0 "" "" 16.8 9.5 0.99 1.7 4.0 "" "" 0.37 +107 288.0 568.5 35.0 1320.0 1.0 "" "" 13.1 9.5 0.99 1.7 2.0 "" "" 0.629 +108 288.0 143.5 35.0 1320.0 1.0 "" "" 11.0 9.5 0.99 1.7 4.0 "" "" 0.026 +109 288.0 743.4 35.0 1320.0 1.0 "" "" 12.6 9.5 0.99 1.7 3.0 "" "" 0.812 +110 288.0 941.9 35.0 1320.0 1.0 "" "" 10.0 9.5 0.99 1.7 2.0 "" "" 0.755 +111 288.0 460.5 35.0 1320.0 1.0 "" "" 10.7 9.5 0.99 1.7 2.0 "" "" 0.355 +112 288.0 1271.8 35.0 1320.0 1.0 "" "" 12.1 9.5 0.99 1.7 2.0 "" "" 0.432 +113 288.0 1308.6 35.0 1320.0 1.0 "" "" 15.7 9.5 0.99 1.7 2.0 "" "" 1.273 +114 288.0 567.9 35.0 1320.0 1.0 "" "" 12.4 9.5 0.99 1.7 3.0 "" "" 0.608 +115 288.0 410.5 35.0 1320.0 1.0 "" "" 84.7 9.5 0.99 1.7 2.0 "" "" 0.057 +116 288.0 391.1 35.0 1320.0 1.0 "" "" 117.3 9.5 0.99 1.7 1.0 "" "" 0.251 +117 288.0 1287.8 35.0 1320.0 1.0 "" "" 9.7 9.5 0.99 1.7 2.0 "" "" 1.592 +118 288.0 634.8 35.0 1320.0 1.0 "" "" 15.9 9.5 0.99 1.7 2.0 "" "" 0.378 +119 288.0 383.0 35.0 1320.0 1.0 "" "" 15.6 9.5 0.99 1.7 2.0 "" "" 0.176 +120 288.0 372.7 35.0 1320.0 1.0 "" "" 16.2 9.5 0.99 1.7 2.0 "" "" 0.167 +121 288.0 237.3 35.0 1320.0 1.0 "" "" 13.3 9.5 0.99 1.7 2.0 "" "" 0.041 +122 288.0 478.8 35.0 1320.0 1.0 "" "" 27.0 9.5 0.99 1.7 2.0 "" "" 0.354 +123 288.0 835.0 35.0 1320.0 1.0 "" "" 51.2 9.5 0.99 1.7 2.0 "" "" 0.735 +124 288.0 282.6 35.0 1320.0 1.0 "" "" 10.6 9.5 0.99 1.7 3.0 "" "" 0.263 +125 288.0 151.4 35.0 1320.0 1.0 "" "" 223.4 9.5 0.99 1.7 3.0 "" "" 0.046 +126 288.0 819.9 35.0 1320.0 1.0 "" "" 13.1 9.5 0.99 1.7 4.0 "" "" 0.75 +127 288.0 162.3 35.0 1320.0 1.0 "" "" 17.3 9.5 0.99 1.7 3.0 "" "" 0.138 +128 288.0 371.1 35.0 1320.0 1.0 "" "" 25.0 9.5 0.99 1.7 1.0 "" "" 0.282 +129 288.0 357.4 35.0 1320.0 1.0 "" "" 16.3 9.5 0.99 1.7 1.0 "" "" 0.262 +130 288.0 331.8 35.0 1320.0 1.0 "" "" 40.2 9.5 0.99 1.7 3.0 "" "" 0.085 +131 288.0 557.5 35.0 1320.0 1.0 "" "" 88.5 9.5 0.99 1.7 3.0 "" "" 0.289 +132 288.0 818.5 35.0 1320.0 1.0 "" "" 9.8 9.5 0.99 1.7 3.0 "" "" 0.66 +133 288.0 1257.6 35.0 1320.0 1.0 "" "" 22.4 9.5 0.99 1.7 3.0 "" "" 0.699 +134 288.0 1116.3 35.0 1320.0 1.0 "" "" 11.6 9.5 0.99 1.7 2.0 "" "" 1.367 +135 288.0 2259.2 35.0 1320.0 1.0 "" "" 12.3 9.5 0.99 1.7 3.0 "" "" 1.338 +136 288.0 307.9 35.0 1320.0 1.0 "" "" 22.6 9.5 0.99 1.7 4.0 "" "" 0.243 +137 288.0 1311.1 35.0 1320.0 1.0 "" "" 24.4 9.5 0.99 1.7 3.0 "" "" 1.712 +138 288.0 848.2 35.0 1320.0 1.0 "" "" 11.6 9.5 0.99 1.7 2.0 "" "" 1.006 +139 288.0 1060.6 35.0 1320.0 1.0 "" "" 10.6 9.5 0.99 1.7 3.0 "" "" 1.108 +140 288.0 785.5 35.0 1320.0 1.0 "" "" 10.0 9.5 0.99 1.7 3.0 "" "" 0.164 +141 288.0 484.9 35.0 1320.0 1.0 "" "" 11.7 9.5 0.99 1.7 0.0 "" "" 0.52 +142 288.0 481.2 35.0 1320.0 1.0 "" "" 9.6 9.5 0.99 1.7 3.0 "" "" 0.424 +143 288.0 484.7 35.0 1320.0 1.0 "" "" 14.9 9.5 0.99 1.7 3.0 "" "" 0.61 +144 288.0 260.6 35.0 1320.0 1.0 "" "" 12.9 9.5 0.99 1.7 3.0 "" "" 0.054 +145 288.0 393.8 35.0 1320.0 1.0 "" "" 12.7 9.5 0.99 1.7 4.0 "" "" 0.291 +146 288.0 273.7 35.0 1320.0 1.0 "" "" 22.7 9.5 0.99 1.7 3.0 "" "" 0.182 +147 288.0 534.4 35.0 1320.0 1.0 "" "" 21.0 9.5 0.99 1.7 3.0 "" "" 0.556 +148 288.0 703.6 35.0 1320.0 1.0 "" "" 10.8 9.5 0.99 1.7 1.0 "" "" 0.195 +149 288.0 335.9 35.0 1320.0 1.0 "" "" 18.5 9.5 0.99 1.7 3.0 "" "" 0.329 +150 288.0 898.1 35.0 1320.0 1.0 "" "" 32.2 9.5 0.99 1.7 2.0 "" "" 0.718 +151 288.0 582.2 35.0 1320.0 1.0 "" "" 13.8 9.5 0.99 1.7 3.0 "" "" 0.571 +152 288.0 636.2 35.0 1320.0 1.0 "" "" 23.1 9.5 0.99 1.7 2.0 "" "" 0.441 +153 288.0 735.7 35.0 1320.0 1.0 "" "" 27.7 9.5 0.99 1.7 2.0 "" "" 0.482 +154 288.0 1405.0 35.0 1320.0 1.0 "" "" 11.4 9.5 0.99 1.7 4.0 "" "" 1.318 +155 288.0 1083.0 35.0 1320.0 1.0 "" "" 19.3 9.5 0.99 1.7 3.0 "" "" 1.101 +156 288.0 709.4 35.0 1320.0 1.0 "" "" 12.4 9.5 0.99 1.7 2.0 "" "" 0.58 +157 288.0 1794.1 35.0 1320.0 1.0 "" "" 14.9 9.5 0.99 1.7 3.0 "" "" 1.849 +158 288.0 736.2 35.0 1320.0 1.0 "" "" 11.0 9.5 0.99 1.7 2.0 "" "" 0.546 +159 288.0 808.6 35.0 1320.0 1.0 "" "" 33.4 9.5 0.99 1.7 2.0 "" "" 0.472 +160 288.0 352.0 35.0 1320.0 1.0 "" "" 16.0 9.5 0.99 1.7 3.0 "" "" 0.126 +161 288.0 447.5 35.0 1320.0 1.0 "" "" 38.3 9.5 0.99 1.7 1.0 "" "" 0.543 +162 288.0 1346.2 35.0 1320.0 1.0 "" "" 12.3 9.5 0.99 1.7 2.0 "" "" 1.567 +163 288.0 428.3 35.0 1320.0 1.0 "" "" 12.3 9.5 0.99 1.7 2.0 "" "" 0.537 +164 288.0 421.8 35.0 1320.0 1.0 "" "" 28.0 9.5 0.99 1.7 4.0 "" "" 0.018 +165 288.0 602.3 35.0 1320.0 1.0 "" "" 16.3 9.5 0.99 1.7 2.0 "" "" 0.093 +166 288.0 1110.1 35.0 1320.0 1.0 "" "" 94.0 9.5 0.99 1.7 3.0 "" "" 0.439 +167 288.0 303.5 35.0 1320.0 1.0 "" "" 10.8 9.5 0.99 1.7 1.0 "" "" 0.137 +168 288.0 799.7 35.0 1320.0 1.0 "" "" 18.8 9.5 0.99 1.7 3.0 "" "" 0.465 +169 288.0 309.6 35.0 1320.0 1.0 "" "" 30.0 9.5 0.99 1.7 2.0 "" "" 0.159 +170 288.0 3446.6 35.0 1320.0 1.0 "" "" 25.6 9.5 0.99 1.7 5.0 "" "" 0.08 +171 288.0 721.5 35.0 1320.0 1.0 "" "" 25.8 9.5 0.99 1.7 2.0 "" "" 0.306 +172 288.0 296.3 35.0 1320.0 1.0 "" "" 21.4 9.5 0.99 1.7 5.0 "" "" 0.134 +173 288.0 573.2 35.0 1320.0 1.0 "" "" 19.6 9.5 0.99 1.7 2.0 "" "" 0.464 +174 288.0 184.5 35.0 1320.0 1.0 "" "" 11.4 9.5 0.99 1.7 1.0 "" "" 0.063 +175 288.0 580.0 35.0 1320.0 1.0 "" "" 15.6 9.5 0.99 1.7 0.0 "" "" 0.632 +176 288.0 754.0 35.0 1320.0 1.0 "" "" 9.6 9.5 0.99 1.7 1.0 "" "" 0.103 +177 288.0 391.9 35.0 1320.0 1.0 "" "" 11.2 9.5 0.99 1.7 1.0 "" "" 0.43 +178 288.0 282.0 35.0 1320.0 1.0 "" "" 21.7 9.5 0.99 1.7 3.0 "" "" 0.073 +179 288.0 548.7 35.0 1320.0 1.0 "" "" 25.8 9.5 0.99 1.7 4.0 "" "" 0.35 +180 288.0 449.7 35.0 1320.0 1.0 "" "" 12.6 9.5 0.99 1.7 3.0 "" "" 0.019 +181 288.0 187.9 35.0 1320.0 1.0 "" "" 40.8 9.5 0.99 1.7 2.0 "" "" 0.052 +182 288.0 522.1 35.0 1320.0 1.0 "" "" 11.8 9.5 0.99 1.7 2.0 "" "" 0.353 +183 288.0 233.3 35.0 1320.0 1.0 "" "" 85.5 9.5 0.99 1.7 2.0 "" "" 0.081 +184 288.0 923.9 35.0 1320.0 1.0 "" "" 9.9 9.5 0.99 1.7 2.0 "" "" 0.961 +185 288.0 568.1 35.0 1320.0 1.0 "" "" 15.9 9.5 0.99 1.7 5.0 "" "" 0.295 +186 288.0 1327.1 35.0 1320.0 1.0 "" "" 18.6 9.5 0.99 1.7 2.0 "" "" 0.437 +187 288.0 901.0 35.0 1320.0 1.0 "" "" 39.8 9.5 0.99 1.7 2.0 "" "" 0.744 +188 288.0 776.9 35.0 1320.0 1.0 "" "" 14.5 9.5 0.99 1.7 2.0 "" "" 0.786 +189 288.0 359.8 35.0 1320.0 1.0 "" "" 46.4 9.5 0.99 1.7 1.0 "" "" 0.372 +190 288.0 733.7 35.0 1320.0 1.0 "" "" 18.7 9.5 0.99 1.7 2.0 "" "" 0.633 +191 288.0 685.6 35.0 1320.0 1.0 "" "" 9.8 9.5 0.99 1.7 3.0 "" "" 0.765 +192 288.0 568.9 35.0 1320.0 1.0 "" "" 14.8 9.5 0.99 1.7 3.0 "" "" 0.644 +193 288.0 398.7 35.0 1320.0 1.0 "" "" 23.3 9.5 0.99 1.7 3.0 "" "" 0.239 +194 288.0 664.2 35.0 1320.0 1.0 "" "" 15.8 9.5 0.99 1.7 3.0 "" "" 0.495 +195 288.0 326.6 35.0 1320.0 1.0 "" "" 14.2 9.5 0.99 1.7 1.0 "" "" 0.251 +196 288.0 726.0 35.0 1320.0 1.0 "" "" 23.7 9.5 0.99 1.7 5.0 "" "" 0.089 +197 288.0 342.1 35.0 1320.0 1.0 "" "" 48.2 9.5 0.99 1.7 2.0 "" "" 0.029 +198 288.0 376.5 35.0 1320.0 1.0 "" "" 47.9 9.5 0.99 1.7 1.0 "" "" 0.279 +199 288.0 271.9 35.0 1320.0 1.0 "" "" 18.9 9.5 0.99 1.7 2.0 "" "" 0.237 +200 288.0 226.2 35.0 1320.0 1.0 "" "" 25.8 9.5 0.99 1.7 2.0 "" "" 0.036 +201 288.0 636.8 35.0 1320.0 1.0 "" "" 18.9 9.5 0.99 1.7 1.0 "" "" 0.069 +202 288.0 1305.4 35.0 1320.0 1.0 "" "" 13.6 9.5 0.99 1.7 1.0 "" "" 1.562 +203 288.0 1160.6 35.0 1320.0 1.0 "" "" 11.9 9.5 0.99 1.7 3.0 "" "" 0.743 +204 288.0 393.9 35.0 1320.0 1.0 "" "" 67.2 9.5 0.99 1.7 2.0 "" "" 0.118 +205 288.0 208.4 35.0 1320.0 1.0 "" "" 9.9 9.5 0.99 1.7 2.0 "" "" 0.2 +206 288.0 747.5 35.0 1320.0 1.0 "" "" 28.0 9.5 0.99 1.7 2.0 "" "" 0.232 +207 288.0 544.7 35.0 1320.0 1.0 "" "" 14.1 9.5 0.99 1.7 2.0 "" "" 0.397 +208 288.0 208.3 35.0 1320.0 1.0 "" "" 25.2 9.5 0.99 1.7 2.0 "" "" 0.125 +209 288.0 647.3 35.0 1320.0 1.0 "" "" 10.2 9.5 0.99 1.7 2.0 "" "" 0.658 +210 288.0 942.2 35.0 1320.0 1.0 "" "" 10.9 9.5 0.99 1.7 2.0 "" "" 0.343 +211 288.0 512.8 35.0 1320.0 1.0 "" "" 24.3 9.5 0.99 1.7 2.0 "" "" 0.053 +212 288.0 407.9 35.0 1320.0 1.0 "" "" 23.2 9.5 0.99 1.7 1.0 "" "" 0.102 +213 288.0 519.0 35.0 1320.0 1.0 "" "" 13.9 9.5 0.99 1.7 2.0 "" "" 0.377 +214 288.0 1342.6 35.0 1320.0 1.0 "" "" 10.9 9.5 0.99 1.7 2.0 "" "" 0.707 +215 288.0 424.8 35.0 1320.0 1.0 "" "" 18.1 9.5 0.99 1.7 4.0 "" "" 0.23 +216 288.0 621.2 35.0 1320.0 1.0 "" "" 16.0 9.5 0.99 1.7 3.0 "" "" 0.79 +217 288.0 655.3 35.0 1320.0 1.0 "" "" 16.5 9.5 0.99 1.7 2.0 "" "" 0.588 +218 288.0 2215.0 35.0 1320.0 1.0 "" "" 19.1 9.5 0.99 1.7 3.0 "" "" 0.08 +219 288.0 716.7 35.0 1320.0 1.0 "" "" 41.8 9.5 0.99 1.7 2.0 "" "" 0.683 +220 288.0 294.3 35.0 1320.0 1.0 "" "" 22.7 9.5 0.99 1.7 4.0 "" "" 0.197 +221 288.0 690.7 35.0 1320.0 1.0 "" "" 20.4 9.5 0.99 1.7 2.0 "" "" 0.703 +222 288.0 794.9 35.0 1320.0 1.0 "" "" 33.2 9.5 0.99 1.7 2.0 "" "" 0.048 +223 288.0 704.5 35.0 1320.0 1.0 "" "" 10.0 9.5 0.99 1.7 3.0 "" "" 0.802 +224 288.0 676.6 35.0 1320.0 1.0 "" "" 11.3 9.5 0.99 1.7 2.0 "" "" 0.153 +225 288.0 458.6 35.0 1320.0 1.0 "" "" 92.5 9.5 0.99 1.7 3.0 "" "" 0.467 +226 288.0 508.2 35.0 1320.0 1.0 "" "" 15.3 9.5 0.99 1.7 3.0 "" "" 0.432 +227 288.0 1294.8 35.0 1320.0 1.0 "" "" 10.6 9.5 0.99 1.7 2.0 "" "" 1.343 +228 288.0 666.7 35.0 1320.0 1.0 "" "" 22.5 9.5 0.99 1.7 0.0 "" "" 0.579 +229 288.0 178.7 35.0 1320.0 1.0 "" "" 22.7 9.5 0.99 1.7 3.0 "" "" 0.054 +230 288.0 154.3 35.0 1320.0 1.0 "" "" 31.8 9.5 0.99 1.7 3.0 "" "" 0.039 +231 288.0 790.1 35.0 1320.0 1.0 "" "" 9.8 9.5 0.99 1.7 2.0 "" "" 1.095 +232 288.0 625.4 35.0 1320.0 1.0 "" "" 11.2 9.5 0.99 1.7 3.0 "" "" 0.622 +233 288.0 448.6 35.0 1320.0 1.0 "" "" 14.1 9.5 0.99 1.7 1.0 "" "" 0.272 +234 288.0 351.6 35.0 1320.0 1.0 "" "" 28.0 9.5 0.99 1.7 1.0 "" "" 0.212 +235 288.0 532.9 35.0 1320.0 1.0 "" "" 14.2 9.5 0.99 1.7 2.0 "" "" 0.502 +236 288.0 146.4 35.0 1320.0 1.0 "" "" 25.9 9.5 0.99 1.7 2.0 "" "" 0.026 +237 288.0 363.8 35.0 1320.0 1.0 "" "" 12.5 9.5 0.99 1.7 1.0 "" "" 0.317 +238 288.0 235.7 35.0 1320.0 1.0 "" "" 47.5 9.5 0.99 1.7 3.0 "" "" 0.182 +239 288.0 394.0 35.0 1320.0 1.0 "" "" 15.3 9.5 0.99 1.7 4.0 "" "" 0.356 +240 288.0 1055.4 35.0 1320.0 1.0 "" "" 27.4 9.5 0.99 1.7 1.0 "" "" 0.935 +241 288.0 212.1 35.0 1320.0 1.0 "" "" 10.6 9.5 0.99 1.7 2.0 "" "" 0.148 +242 288.0 2097.3 35.0 1320.0 1.0 "" "" 9.9 9.5 0.99 1.7 1.0 "" "" 2.036 +243 288.0 440.9 35.0 1320.0 1.0 "" "" 13.3 9.5 0.99 1.7 2.0 "" "" 0.405 +244 288.0 677.6 35.0 1320.0 1.0 "" "" 16.1 9.5 0.99 1.7 3.0 "" "" 0.31 +245 288.0 339.0 35.0 1320.0 1.0 "" "" 10.6 9.5 0.99 1.7 1.0 "" "" 0.042 +246 288.0 270.0 35.0 1320.0 1.0 "" "" 12.0 9.5 0.99 1.7 4.0 "" "" 0.033 +247 288.0 430.7 35.0 1320.0 1.0 "" "" 15.2 9.5 0.99 1.7 2.0 "" "" 0.464 +248 288.0 2151.2 35.0 1320.0 1.0 "" "" 25.4 9.5 0.99 1.7 1.0 "" "" 0.622 +249 288.0 445.0 35.0 1320.0 1.0 "" "" 14.0 9.5 0.99 1.7 4.0 "" "" 0.301 +250 288.0 326.7 35.0 1320.0 1.0 "" "" 16.6 9.5 0.99 1.7 3.0 "" "" 0.229 +251 288.0 756.5 35.0 1320.0 1.0 "" "" 12.1 9.5 0.99 1.7 4.0 "" "" 0.619 +252 288.0 474.3 35.0 1320.0 1.0 "" "" 11.6 9.5 0.99 1.7 4.0 "" "" 0.136 +253 288.0 301.1 35.0 1320.0 1.0 "" "" 24.9 9.5 0.99 1.7 3.0 "" "" 0.08 +254 288.0 251.3 35.0 1320.0 1.0 "" "" 34.5 9.5 0.99 1.7 3.0 "" "" 0.19 +255 288.0 215.4 35.0 1320.0 1.0 "" "" 66.6 9.5 0.99 1.7 2.0 "" "" 0.152 +256 288.0 688.1 35.0 1320.0 1.0 "" "" 11.4 9.5 0.99 1.7 2.0 "" "" 0.562 +257 288.0 395.1 35.0 1320.0 1.0 "" "" 19.7 9.5 0.99 1.7 4.0 "" "" 0.234 +258 288.0 1602.7 35.0 1320.0 1.0 "" "" 20.4 9.5 0.99 1.7 2.0 "" "" 1.216 +259 288.0 409.3 35.0 1320.0 1.0 "" "" 14.4 9.5 0.99 1.7 3.0 "" "" 0.196 +260 288.0 785.5 35.0 1320.0 1.0 "" "" 25.5 9.5 0.99 1.7 2.0 "" "" 0.821 +261 288.0 1411.5 35.0 1320.0 1.0 "" "" 10.5 9.5 0.99 1.7 2.0 "" "" 0.908 +262 288.0 365.7 35.0 1320.0 1.0 "" "" 29.0 9.5 0.99 1.7 1.0 "" "" 0.208 +263 288.0 809.8 35.0 1320.0 1.0 "" "" 10.1 9.5 0.99 1.7 3.0 "" "" 0.106 +264 288.0 377.8 35.0 1320.0 1.0 "" "" 13.8 9.5 0.99 1.7 2.0 "" "" 0.139 +265 288.0 422.9 35.0 1320.0 1.0 "" "" 12.2 9.5 0.99 1.7 1.0 "" "" 0.423 +266 288.0 295.9 35.0 1320.0 1.0 "" "" 21.9 9.5 0.99 1.7 2.0 "" "" 0.215 +267 288.0 199.7 35.0 1320.0 1.0 "" "" 39.4 9.5 0.99 1.7 2.0 "" "" 0.161 +268 288.0 456.5 35.0 1320.0 1.0 "" "" 12.2 9.5 0.99 1.7 2.0 "" "" 0.21 +269 288.0 587.6 35.0 1320.0 1.0 "" "" 10.8 9.5 0.99 1.7 3.0 "" "" 0.055 +270 288.0 275.5 35.0 1320.0 1.0 "" "" 10.2 9.5 0.99 1.7 2.0 "" "" 0.096 +271 288.0 386.9 35.0 1320.0 1.0 "" "" 18.2 9.5 0.99 1.7 1.0 "" "" 0.053 +272 288.0 1319.3 35.0 1320.0 1.0 "" "" 10.1 9.5 0.99 1.7 2.0 "" "" 1.418 +273 288.0 629.7 35.0 1320.0 1.0 "" "" 26.0 9.5 0.99 1.7 2.0 "" "" 0.741 +274 288.0 157.7 35.0 1320.0 1.0 "" "" 21.4 9.5 0.99 1.7 2.0 "" "" 0.024 +275 288.0 722.1 35.0 1320.0 1.0 "" "" 27.7 9.5 0.99 1.7 4.0 "" "" 0.616 +276 288.0 775.6 35.0 1320.0 1.0 "" "" 22.5 9.5 0.99 1.7 3.0 "" "" 0.609 +277 288.0 476.8 35.0 1320.0 1.0 "" "" 9.8 9.5 0.99 1.7 3.0 "" "" 0.393 +278 288.0 823.4 35.0 1320.0 1.0 "" "" 12.4 9.5 0.99 1.7 2.0 "" "" 0.801 +279 288.0 709.3 35.0 1320.0 1.0 "" "" 17.7 9.5 0.99 1.7 3.0 "" "" 0.796 +280 288.0 765.4 35.0 1320.0 1.0 "" "" 9.9 9.5 0.99 1.7 1.0 "" "" 1.032 +281 288.0 1306.7 35.0 1320.0 1.0 "" "" 28.2 9.5 0.99 1.7 3.0 "" "" 0.336 +282 288.0 253.7 35.0 1320.0 1.0 "" "" 36.1 9.5 0.99 1.7 3.0 "" "" 0.066 +283 288.0 762.3 35.0 1320.0 1.0 "" "" 10.5 9.5 0.99 1.7 4.0 "" "" 0.667 +284 288.0 618.1 35.0 1320.0 1.0 "" "" 12.9 9.5 0.99 1.7 2.0 "" "" 0.586 +285 288.0 1062.3 35.0 1320.0 1.0 "" "" 71.5 9.5 0.99 1.7 0.0 "" "" 0.792 +286 288.0 578.1 35.0 1320.0 1.0 "" "" 17.7 9.5 0.99 1.7 2.0 "" "" 0.532 +287 288.0 679.1 35.0 1320.0 1.0 "" "" 12.4 9.5 0.99 1.7 2.0 "" "" 0.338 +288 288.0 330.7 35.0 1320.0 1.0 "" "" 75.1 9.5 0.99 1.7 3.0 "" "" 0.363 +289 288.0 312.7 35.0 1320.0 1.0 "" "" 13.9 9.5 0.99 1.7 4.0 "" "" 0.059 +290 288.0 165.8 35.0 1320.0 1.0 "" "" 16.7 9.5 0.99 1.7 1.0 "" "" 0.022 +291 288.0 1139.2 35.0 1320.0 1.0 "" "" 18.3 9.5 0.99 1.7 2.0 "" "" 1.241 +292 288.0 320.1 35.0 1320.0 1.0 "" "" 88.2 9.5 0.99 1.7 2.0 "" "" 0.29 +293 288.0 700.6 35.0 1320.0 1.0 "" "" 13.4 9.5 0.99 1.7 2.0 "" "" 0.921 +294 288.0 246.4 35.0 1320.0 1.0 "" "" 23.4 9.5 0.99 1.7 3.0 "" "" 0.123 +295 288.0 283.5 35.0 1320.0 1.0 "" "" 16.0 9.5 0.99 1.7 0.0 "" "" 0.085 +296 288.0 406.3 35.0 1320.0 1.0 "" "" 12.0 9.5 0.99 1.7 0.0 "" "" 0.275 +297 288.0 1543.3 35.0 1320.0 1.0 "" "" 9.7 9.5 0.99 1.7 3.0 "" "" 1.742 +298 288.0 511.2 35.0 1320.0 1.0 "" "" 18.6 9.5 0.99 1.7 2.0 "" "" 0.268 +299 288.0 271.5 35.0 1320.0 1.0 "" "" 9.9 9.5 0.99 1.7 1.0 "" "" 0.079 +300 288.0 449.7 35.0 1320.0 1.0 "" "" 10.7 9.5 0.99 1.7 1.0 "" "" 0.622 +301 288.0 900.2 35.0 1320.0 1.0 "" "" 9.9 9.5 0.99 1.7 3.0 "" "" 0.15 +302 288.0 863.7 35.0 1320.0 1.0 "" "" 37.9 9.5 0.99 1.7 2.0 "" "" 0.837 +303 288.0 275.3 35.0 1320.0 1.0 "" "" 18.8 9.5 0.99 1.7 3.0 "" "" 0.104 +304 288.0 179.1 35.0 1320.0 1.0 "" "" 109.1 9.5 0.99 1.7 3.0 "" "" 0.001 +305 288.0 814.0 35.0 1320.0 1.0 "" "" 11.6 9.5 0.99 1.7 3.0 "" "" 0.42 +306 288.0 201.2 35.0 1320.0 1.0 "" "" 13.7 9.5 0.99 1.7 1.0 "" "" 0.01 +307 288.0 517.9 35.0 1320.0 1.0 "" "" 11.2 9.5 0.99 1.7 4.0 "" "" 0.633 +308 288.0 548.6 35.0 1320.0 1.0 "" "" 11.9 9.5 0.99 1.7 2.0 "" "" 0.47 +309 288.0 385.2 35.0 1320.0 1.0 "" "" 55.9 9.5 0.99 1.7 2.0 "" "" 0.227 +310 288.0 347.6 35.0 1320.0 1.0 "" "" 12.2 9.5 0.99 1.7 3.0 "" "" 0.17 +311 288.0 266.5 35.0 1320.0 1.0 "" "" 15.9 9.5 0.99 1.7 4.0 "" "" 0.068 +312 288.0 359.8 35.0 1320.0 1.0 "" "" 13.7 9.5 0.99 1.7 2.0 "" "" 0.362 +313 288.0 1062.6 35.0 1320.0 1.0 "" "" 11.0 9.5 0.99 1.7 2.0 "" "" 0.864 +314 288.0 602.4 35.0 1320.0 1.0 "" "" 27.8 9.5 0.99 1.7 1.0 "" "" 0.39 +315 288.0 615.3 35.0 1320.0 1.0 "" "" 13.1 9.5 0.99 1.7 2.0 "" "" 0.815 +316 288.0 930.0 35.0 1320.0 1.0 "" "" 19.8 9.5 0.99 1.7 4.0 "" "" 0.49 +317 288.0 652.9 35.0 1320.0 1.0 "" "" 12.2 9.5 0.99 1.7 5.0 "" "" 0.633 +318 288.0 634.4 35.0 1320.0 1.0 "" "" 20.7 9.5 0.99 1.7 2.0 "" "" 0.407 +319 288.0 747.8 35.0 1320.0 1.0 "" "" 9.6 9.5 0.99 1.7 4.0 "" "" 0.086 +320 288.0 596.2 35.0 1320.0 1.0 "" "" 21.3 9.5 0.99 1.7 2.0 "" "" 0.603 +321 288.0 469.1 35.0 1320.0 1.0 "" "" 52.5 9.5 0.99 1.7 3.0 "" "" 0.387 +322 288.0 1028.2 35.0 1320.0 1.0 "" "" 11.3 9.5 0.99 1.7 2.0 "" "" 0.645 +323 288.0 430.6 35.0 1320.0 1.0 "" "" 12.5 9.5 0.99 1.7 0.0 "" "" 0.345 +324 288.0 1121.6 35.0 1320.0 1.0 "" "" 11.2 9.5 0.99 1.7 3.0 "" "" 1.146 +325 288.0 403.1 35.0 1320.0 1.0 "" "" 26.6 9.5 0.99 1.7 3.0 "" "" 0.284 +326 288.0 563.6 35.0 1320.0 1.0 "" "" 22.1 9.5 0.99 1.7 1.0 "" "" 0.689 +327 288.0 1644.2 35.0 1320.0 1.0 "" "" 13.4 9.5 0.99 1.7 3.0 "" "" 2.063 +328 288.0 389.5 35.0 1320.0 1.0 "" "" 23.9 9.5 0.99 1.7 3.0 "" "" 0.307 +329 288.0 624.6 35.0 1320.0 1.0 "" "" 62.3 9.5 0.99 1.7 2.0 "" "" 0.159 +330 288.0 490.1 35.0 1320.0 1.0 "" "" 14.0 9.5 0.99 1.7 2.0 "" "" 0.532 +331 288.0 912.3 35.0 1320.0 1.0 "" "" 15.9 9.5 0.99 1.7 3.0 "" "" 0.645 +332 288.0 692.6 35.0 1320.0 1.0 "" "" 27.3 9.5 0.99 1.7 2.0 "" "" 0.798 +333 288.0 179.2 35.0 1320.0 1.0 "" "" 18.5 9.5 0.99 1.7 3.0 "" "" 0.103 +334 288.0 780.0 35.0 1320.0 1.0 "" "" 17.7 9.5 0.99 1.7 4.0 "" "" 0.408 +335 288.0 964.2 35.0 1320.0 1.0 "" "" 11.6 9.5 0.99 1.7 1.0 "" "" 1.173 +336 288.0 203.2 35.0 1320.0 1.0 "" "" 14.0 9.5 0.99 1.7 3.0 "" "" 0.092 +337 288.0 698.1 35.0 1320.0 1.0 "" "" 38.8 9.5 0.99 1.7 3.0 "" "" 0.46 +338 288.0 338.5 35.0 1320.0 1.0 "" "" 15.2 9.5 0.99 1.7 3.0 "" "" 0.318 +339 288.0 520.4 35.0 1320.0 1.0 "" "" 12.8 9.5 0.99 1.7 2.0 "" "" 0.439 +340 288.0 958.2 35.0 1320.0 1.0 "" "" 12.3 9.5 0.99 1.7 2.0 "" "" 0.289 +341 288.0 576.5 35.0 1320.0 1.0 "" "" 24.2 9.5 0.99 1.7 3.0 "" "" 0.514 +342 288.0 418.5 35.0 1320.0 1.0 "" "" 22.6 9.5 0.99 1.7 3.0 "" "" 0.37 +343 288.0 296.4 35.0 1320.0 1.0 "" "" 18.3 9.5 0.99 1.7 3.0 "" "" 0.053 +344 288.0 666.2 35.0 1320.0 1.0 "" "" 12.6 9.5 0.99 1.7 1.0 "" "" 0.846 +345 288.0 315.6 35.0 1320.0 1.0 "" "" 20.5 9.5 0.99 1.7 3.0 "" "" 0.285 +346 288.0 140.5 35.0 1320.0 1.0 "" "" 12.0 9.5 0.99 1.7 0.0 "" "" 0.036 +347 288.0 764.2 35.0 1320.0 1.0 "" "" 9.5 9.5 0.99 1.7 1.0 "" "" 0.951 +348 288.0 841.2 35.0 1320.0 1.0 "" "" 14.6 9.5 0.99 1.7 0.0 "" "" 0.603 +349 288.0 938.2 35.0 1320.0 1.0 "" "" 20.4 9.5 0.99 1.7 3.0 "" "" 0.192 +350 288.0 489.1 35.0 1320.0 1.0 "" "" 13.3 9.5 0.99 1.7 1.0 "" "" 0.241 +351 288.0 655.5 35.0 1320.0 1.0 "" "" 12.3 9.5 0.99 1.7 2.0 "" "" 0.442 +352 288.0 552.8 35.0 1320.0 1.0 "" "" 71.4 9.5 0.99 1.7 1.0 "" "" 0.194 +353 288.0 2749.7 35.0 1320.0 1.0 "" "" 9.9 9.5 0.99 1.7 0.0 "" "" 2.304 +354 288.0 3004.1 35.0 1320.0 1.0 "" "" 14.3 9.5 0.99 1.7 3.0 "" "" 0.315 +355 288.0 668.2 35.0 1320.0 1.0 "" "" 33.2 9.5 0.99 1.7 3.0 "" "" 0.132 +356 288.0 663.6 35.0 1320.0 1.0 "" "" 19.0 9.5 0.99 1.7 2.0 "" "" 0.575 +357 288.0 564.6 35.0 1320.0 1.0 "" "" 86.8 9.5 0.99 1.7 4.0 "" "" 0.333 +358 288.0 485.8 35.0 1320.0 1.0 "" "" 19.0 9.5 0.99 1.7 1.0 "" "" 0.378 +359 288.0 639.6 35.0 1320.0 1.0 "" "" 131.0 9.5 0.99 1.7 2.0 "" "" 0.238 +360 288.0 94.2 35.0 1320.0 1.0 "" "" 23.9 9.5 0.99 1.7 2.0 "" "" 0.003 +361 288.0 1468.8 35.0 1320.0 1.0 "" "" 29.3 9.5 0.99 1.7 3.0 "" "" 0.241 +362 288.0 215.7 35.0 1320.0 1.0 "" "" 20.8 9.5 0.99 1.7 2.0 "" "" 0.081 +363 288.0 969.2 35.0 1320.0 1.0 "" "" 16.2 9.5 0.99 1.7 2.0 "" "" 0.571 +364 288.0 1324.9 35.0 1320.0 1.0 "" "" 12.0 9.5 0.99 1.7 5.0 "" "" 1.313 +365 288.0 426.8 35.0 1320.0 1.0 "" "" 20.7 9.5 0.99 1.7 4.0 "" "" 0.429 +366 288.0 524.1 35.0 1320.0 1.0 "" "" 28.2 9.5 0.99 1.7 3.0 "" "" 0.06 +367 288.0 686.1 35.0 1320.0 1.0 "" "" 24.5 9.5 0.99 1.7 2.0 "" "" 0.764 +368 288.0 1243.9 35.0 1320.0 1.0 "" "" 14.0 9.5 0.99 1.7 2.0 "" "" 1.332 +369 288.0 875.0 35.0 1320.0 1.0 "" "" 23.3 9.5 0.99 1.7 2.0 "" "" 0.899 +370 288.0 1351.9 35.0 1320.0 1.0 "" "" 24.4 9.5 0.99 1.7 4.0 "" "" 1.325 +371 288.0 402.8 35.0 1320.0 1.0 "" "" 40.1 9.5 0.99 1.7 2.0 "" "" 0.171 +372 288.0 884.3 35.0 1320.0 1.0 "" "" 9.6 9.5 0.99 1.7 3.0 "" "" 0.408 +373 288.0 411.0 35.0 1320.0 1.0 "" "" 11.8 9.5 0.99 1.7 4.0 "" "" 0.022 +374 288.0 494.9 35.0 1320.0 1.0 "" "" 9.8 9.5 0.99 1.7 3.0 "" "" 0.516 +375 288.0 645.2 35.0 1320.0 1.0 "" "" 12.5 9.5 0.99 1.7 2.0 "" "" 0.607 +376 288.0 504.0 35.0 1320.0 1.0 "" "" 12.2 9.5 0.99 1.7 1.0 "" "" 0.452 +377 288.0 593.8 35.0 1320.0 1.0 "" "" 12.0 9.5 0.99 1.7 3.0 "" "" 0.62 +378 288.0 491.2 35.0 1320.0 1.0 "" "" 21.8 9.5 0.99 1.7 3.0 "" "" 0.141 +379 288.0 395.1 35.0 1320.0 1.0 "" "" 16.3 9.5 0.99 1.7 3.0 "" "" 0.429 +380 288.0 566.1 35.0 1320.0 1.0 "" "" 10.0 9.5 0.99 1.7 3.0 "" "" 0.515 +381 288.0 181.1 35.0 1320.0 1.0 "" "" 22.5 9.5 0.99 1.7 2.0 "" "" 0.065 +382 288.0 532.3 35.0 1320.0 1.0 "" "" 11.1 9.5 0.99 1.7 2.0 "" "" 0.59 +383 288.0 991.9 35.0 1320.0 1.0 "" "" 15.6 9.5 0.99 1.7 2.0 "" "" 1.222 +384 288.0 1035.4 35.0 1320.0 1.0 "" "" 27.8 9.5 0.99 1.7 3.0 "" "" 1.114 +385 288.0 715.9 35.0 1320.0 1.0 "" "" 19.1 9.5 0.99 1.7 4.0 "" "" 0.09 +386 288.0 691.9 35.0 1320.0 1.0 "" "" 9.9 9.5 0.99 1.7 4.0 "" "" 0.696 +387 288.0 1031.6 35.0 1320.0 1.0 "" "" 10.3 9.5 0.99 1.7 4.0 "" "" 1.312 +388 288.0 181.0 35.0 1320.0 1.0 "" "" 26.0 9.5 0.99 1.7 2.0 "" "" 0.136 +389 288.0 362.4 35.0 1320.0 1.0 "" "" 10.0 9.5 0.99 1.7 2.0 "" "" 0.287 +390 288.0 503.6 35.0 1320.0 1.0 "" "" 29.2 9.5 0.99 1.7 3.0 "" "" 0.639 +391 288.0 667.5 35.0 1320.0 1.0 "" "" 13.7 9.5 0.99 1.7 4.0 "" "" 0.128 +392 288.0 765.5 35.0 1320.0 1.0 "" "" 19.0 9.5 0.99 1.7 3.0 "" "" 0.742 +393 288.0 135.7 35.0 1320.0 1.0 "" "" 27.9 9.5 0.99 1.7 4.0 "" "" 0.039 +394 288.0 599.2 35.0 1320.0 1.0 "" "" 24.3 9.5 0.99 1.7 4.0 "" "" 0.24 +395 288.0 1321.2 35.0 1320.0 1.0 "" "" 12.3 9.5 0.99 1.7 2.0 "" "" 0.407 +396 288.0 388.5 35.0 1320.0 1.0 "" "" 10.1 9.5 0.99 1.7 2.0 "" "" 0.135 +397 288.0 269.0 35.0 1320.0 1.0 "" "" 30.0 9.5 0.99 1.7 4.0 "" "" 0.123 +398 288.0 944.6 35.0 1320.0 1.0 "" "" 23.0 9.5 0.99 1.7 2.0 "" "" 0.874 +399 288.0 259.8 35.0 1320.0 1.0 "" "" 29.0 9.5 0.99 1.7 2.0 "" "" 0.019 +400 288.0 916.6 35.0 1320.0 1.0 "" "" 14.4 9.5 0.99 1.7 3.0 "" "" 0.997 +401 288.0 266.6 35.0 1320.0 1.0 "" "" 10.5 9.5 0.99 1.7 2.0 "" "" 0.241 +402 288.0 451.4 35.0 1320.0 1.0 "" "" 17.4 9.5 0.99 1.7 3.0 "" "" 0.454 +403 288.0 724.2 35.0 1320.0 1.0 "" "" 32.0 9.5 0.99 1.7 2.0 "" "" 0.793 +404 288.0 187.5 35.0 1320.0 1.0 "" "" 16.4 9.5 0.99 1.7 3.0 "" "" 0.046 +405 288.0 485.5 35.0 1320.0 1.0 "" "" 20.3 9.5 0.99 1.7 4.0 "" "" 0.162 +406 288.0 236.1 35.0 1320.0 1.0 "" "" 18.3 9.5 0.99 1.7 3.0 "" "" 0.142 +407 288.0 730.6 35.0 1320.0 1.0 "" "" 41.2 9.5 0.99 1.7 3.0 "" "" 0.72 +408 288.0 272.6 35.0 1320.0 1.0 "" "" 39.8 9.5 0.99 1.7 3.0 "" "" 0.141 +409 288.0 524.5 35.0 1320.0 1.0 "" "" 11.7 9.5 0.99 1.7 0.0 "" "" 0.623 +410 288.0 489.3 35.0 1320.0 1.0 "" "" 15.3 9.5 0.99 1.7 1.0 "" "" 0.446 +411 288.0 448.1 35.0 1320.0 1.0 "" "" 12.7 9.5 0.99 1.7 2.0 "" "" 0.467 +412 288.0 1045.2 35.0 1320.0 1.0 "" "" 27.5 9.5 0.99 1.7 4.0 "" "" 1.06 +413 288.0 958.7 35.0 1320.0 1.0 "" "" 23.9 9.5 0.99 1.7 3.0 "" "" 1.035 +414 288.0 233.6 35.0 1320.0 1.0 "" "" 9.6 9.5 0.99 1.7 3.0 "" "" 0.075 +415 288.0 659.9 35.0 1320.0 1.0 "" "" 10.5 9.5 0.99 1.7 2.0 "" "" 0.318 +416 288.0 635.2 35.0 1320.0 1.0 "" "" 22.9 9.5 0.99 1.7 3.0 "" "" 0.418 +417 288.0 2488.2 35.0 1320.0 1.0 "" "" 22.0 9.5 0.99 1.7 3.0 "" "" 2.001 +418 288.0 416.8 35.0 1320.0 1.0 "" "" 9.6 9.5 0.99 1.7 4.0 "" "" 0.462 +419 288.0 460.9 35.0 1320.0 1.0 "" "" 76.9 9.5 0.99 1.7 2.0 "" "" 0.164 +420 288.0 559.6 35.0 1320.0 1.0 "" "" 14.7 9.5 0.99 1.7 4.0 "" "" 0.72 +421 288.0 878.5 35.0 1320.0 1.0 "" "" 12.7 9.5 0.99 1.7 3.0 "" "" 0.631 +422 288.0 836.2 35.0 1320.0 1.0 "" "" 10.6 9.5 0.99 1.7 1.0 "" "" 0.306 +423 288.0 717.2 35.0 1320.0 1.0 "" "" 10.3 9.5 0.99 1.7 2.0 "" "" 0.901 +424 288.0 203.8 35.0 1320.0 1.0 "" "" 50.4 9.5 0.99 1.7 4.0 "" "" 0.008 +425 288.0 332.5 35.0 1320.0 1.0 "" "" 10.0 9.5 0.99 1.7 2.0 "" "" 0.255 +426 288.0 513.5 35.0 1320.0 1.0 "" "" 11.9 9.5 0.99 1.7 3.0 "" "" 0.412 +427 288.0 890.1 35.0 1320.0 1.0 "" "" 17.0 9.5 0.99 1.7 2.0 "" "" 1.106 +428 288.0 1119.1 35.0 1320.0 1.0 "" "" 11.6 9.5 0.99 1.7 1.0 "" "" 1.247 +429 288.0 692.3 35.0 1320.0 1.0 "" "" 17.5 9.5 0.99 1.7 3.0 "" "" 0.665 +430 288.0 794.0 35.0 1320.0 1.0 "" "" 56.4 9.5 0.99 1.7 4.0 "" "" 0.265 +431 288.0 692.8 35.0 1320.0 1.0 "" "" 11.1 9.5 0.99 1.7 3.0 "" "" 0.926 +432 288.0 529.2 35.0 1320.0 1.0 "" "" 111.0 9.5 0.99 1.7 2.0 "" "" 0.226 +433 288.0 476.0 35.0 1320.0 1.0 "" "" 12.5 9.5 0.99 1.7 4.0 "" "" 0.458 +434 288.0 439.2 35.0 1320.0 1.0 "" "" 10.9 9.5 0.99 1.7 1.0 "" "" 0.27 +435 288.0 842.2 35.0 1320.0 1.0 "" "" 19.2 9.5 0.99 1.7 1.0 "" "" 0.772 +436 288.0 644.8 35.0 1320.0 1.0 "" "" 26.2 9.5 0.99 1.7 1.0 "" "" 0.709 +437 288.0 389.8 35.0 1320.0 1.0 "" "" 13.5 9.5 0.99 1.7 1.0 "" "" 0.112 +438 288.0 363.0 35.0 1320.0 1.0 "" "" 62.7 9.5 0.99 1.7 0.0 "" "" 0.166 +439 288.0 1050.3 35.0 1320.0 1.0 "" "" 26.1 9.5 0.99 1.7 2.0 "" "" 0.544 +440 288.0 182.9 35.0 1320.0 1.0 "" "" 19.0 9.5 0.99 1.7 1.0 "" "" 0.002 +441 288.0 686.3 35.0 1320.0 1.0 "" "" 13.3 9.5 0.99 1.7 2.0 "" "" 0.151 +442 288.0 335.0 35.0 1320.0 1.0 "" "" 13.5 9.5 0.99 1.7 4.0 "" "" 0.104 +443 288.0 613.8 35.0 1320.0 1.0 "" "" 14.1 9.5 0.99 1.7 3.0 "" "" 0.791 +444 288.0 317.5 35.0 1320.0 1.0 "" "" 11.4 9.5 0.99 1.7 2.0 "" "" 0.274 +445 288.0 427.7 35.0 1320.0 1.0 "" "" 25.2 9.5 0.99 1.7 4.0 "" "" 0.551 +446 288.0 155.8 35.0 1320.0 1.0 "" "" 20.2 9.5 0.99 1.7 4.0 "" "" 0.075 +447 288.0 988.1 35.0 1320.0 1.0 "" "" 13.3 9.5 0.99 1.7 3.0 "" "" 1.024 +448 288.0 256.9 35.0 1320.0 1.0 "" "" 11.8 9.5 0.99 1.7 3.0 "" "" 0.065 +449 288.0 412.3 35.0 1320.0 1.0 "" "" 48.7 9.5 0.99 1.7 2.0 "" "" 0.343 +450 288.0 595.9 35.0 1320.0 1.0 "" "" 69.4 9.5 0.99 1.7 4.0 "" "" 0.255 +451 288.0 210.9 35.0 1320.0 1.0 "" "" 113.3 9.5 0.99 1.7 3.0 "" "" 0.012 +452 288.0 463.7 35.0 1320.0 1.0 "" "" 14.9 9.5 0.99 1.7 2.0 "" "" 0.245 +453 288.0 831.0 35.0 1320.0 1.0 "" "" 9.8 9.5 0.99 1.7 3.0 "" "" 0.714 +454 288.0 3302.9 35.0 1320.0 1.0 "" "" 10.4 9.5 0.99 1.7 4.0 "" "" 1.408 +455 288.0 924.3 35.0 1320.0 1.0 "" "" 16.5 9.5 0.99 1.7 3.0 "" "" 0.244 +456 288.0 625.2 35.0 1320.0 1.0 "" "" 15.8 9.5 0.99 1.7 3.0 "" "" 0.519 +457 288.0 836.0 35.0 1320.0 1.0 "" "" 28.4 9.5 0.99 1.7 2.0 "" "" 0.497 +458 288.0 1100.8 35.0 1320.0 1.0 "" "" 12.2 9.5 0.99 1.7 2.0 "" "" 1.079 +459 288.0 380.1 35.0 1320.0 1.0 "" "" 16.6 9.5 0.99 1.7 3.0 "" "" 0.149 +460 288.0 615.4 35.0 1320.0 1.0 "" "" 15.5 9.5 0.99 1.7 2.0 "" "" 0.215 +461 288.0 786.4 35.0 1320.0 1.0 "" "" 77.7 9.5 0.99 1.7 2.0 "" "" 0.364 +462 288.0 460.6 35.0 1320.0 1.0 "" "" 12.4 9.5 0.99 1.7 2.0 "" "" 0.153 +463 288.0 734.3 35.0 1320.0 1.0 "" "" 14.9 9.5 0.99 1.7 2.0 "" "" 0.393 +464 288.0 1370.5 35.0 1320.0 1.0 "" "" 16.2 9.5 0.99 1.7 2.0 "" "" 0.292 +465 288.0 463.3 35.0 1320.0 1.0 "" "" 13.3 9.5 0.99 1.7 4.0 "" "" 0.344 +466 288.0 250.4 35.0 1320.0 1.0 "" "" 11.0 9.5 0.99 1.7 2.0 "" "" 0.001 +467 288.0 478.9 35.0 1320.0 1.0 "" "" 10.8 9.5 0.99 1.7 4.0 "" "" 0.471 +468 288.0 164.9 35.0 1320.0 1.0 "" "" 48.7 9.5 0.99 1.7 1.0 "" "" 0.02 +469 288.0 331.3 35.0 1320.0 1.0 "" "" 36.3 9.5 0.99 1.7 3.0 "" "" 0.336 +470 288.0 940.2 35.0 1320.0 1.0 "" "" 18.3 9.5 0.99 1.7 4.0 "" "" 0.651 +471 288.0 740.2 35.0 1320.0 1.0 "" "" 9.7 9.5 0.99 1.7 2.0 "" "" 0.844 +472 288.0 677.1 35.0 1320.0 1.0 "" "" 12.0 9.5 0.99 1.7 2.0 "" "" 0.575 +473 288.0 910.6 35.0 1320.0 1.0 "" "" 10.3 9.5 0.99 1.7 2.0 "" "" 0.963 +474 288.0 552.9 35.0 1320.0 1.0 "" "" 13.9 9.5 0.99 1.7 2.0 "" "" 0.287 +475 288.0 342.1 35.0 1320.0 1.0 "" "" 201.4 9.5 0.99 1.7 1.0 "" "" 0.315 +476 288.0 323.7 35.0 1320.0 1.0 "" "" 31.2 9.5 0.99 1.7 2.0 "" "" 0.18 +477 288.0 553.2 35.0 1320.0 1.0 "" "" 29.9 9.5 0.99 1.7 1.0 "" "" 0.354 +478 288.0 541.8 35.0 1320.0 1.0 "" "" 10.2 9.5 0.99 1.7 3.0 "" "" 0.428 +479 288.0 330.2 35.0 1320.0 1.0 "" "" 46.8 9.5 0.99 1.7 2.0 "" "" 0.326 +480 288.0 518.4 35.0 1320.0 1.0 "" "" 18.8 9.5 0.99 1.7 4.0 "" "" 0.364 +481 288.0 381.3 35.0 1320.0 1.0 "" "" 36.2 9.5 0.99 1.7 4.0 "" "" 0.297 +482 288.0 1405.2 35.0 1320.0 1.0 "" "" 15.4 9.5 0.99 1.7 2.0 "" "" 1.059 +483 288.0 282.5 35.0 1320.0 1.0 "" "" 14.5 9.5 0.99 1.7 3.0 "" "" 0.209 +484 288.0 610.2 35.0 1320.0 1.0 "" "" 35.2 9.5 0.99 1.7 3.0 "" "" 0.515 +485 288.0 270.6 35.0 1320.0 1.0 "" "" 14.5 9.5 0.99 1.7 3.0 "" "" 0.323 +486 288.0 375.0 35.0 1320.0 1.0 "" "" 30.8 9.5 0.99 1.7 2.0 "" "" 0.249 +487 288.0 982.7 35.0 1320.0 1.0 "" "" 10.0 9.5 0.99 1.7 2.0 "" "" 0.836 +488 288.0 248.4 35.0 1320.0 1.0 "" "" 11.2 9.5 0.99 1.7 1.0 "" "" 0.076 +489 288.0 2137.1 35.0 1320.0 1.0 "" "" 15.4 9.5 0.99 1.7 5.0 "" "" 0.049 +490 288.0 423.0 35.0 1320.0 1.0 "" "" 11.5 9.5 0.99 1.7 3.0 "" "" 0.311 +491 288.0 494.6 35.0 1320.0 1.0 "" "" 12.8 9.5 0.99 1.7 3.0 "" "" 0.116 +492 288.0 211.5 35.0 1320.0 1.0 "" "" 12.8 9.5 0.99 1.7 2.0 "" "" 0.145 +493 288.0 871.3 35.0 1320.0 1.0 "" "" 25.3 9.5 0.99 1.7 5.0 "" "" 0.917 +494 288.0 864.6 35.0 1320.0 1.0 "" "" 10.3 9.5 0.99 1.7 3.0 "" "" 0.557 +495 288.0 960.7 35.0 1320.0 1.0 "" "" 9.9 9.5 0.99 1.7 2.0 "" "" 0.209 +496 288.0 377.2 35.0 1320.0 1.0 "" "" 136.5 9.5 0.99 1.7 2.0 "" "" 0.223 +497 288.0 420.1 35.0 1320.0 1.0 "" "" 14.1 9.5 0.99 1.7 2.0 "" "" 0.275 +498 288.0 212.0 35.0 1320.0 1.0 "" "" 11.5 9.5 0.99 1.7 4.0 "" "" 0.022 +499 288.0 301.6 35.0 1320.0 1.0 "" "" 9.6 9.5 0.99 1.7 0.0 "" "" 0.172 +500 288.0 892.1 35.0 1320.0 1.0 "" "" 17.3 9.5 0.99 1.7 3.0 "" "" 1.031 +501 288.0 491.5 35.0 1320.0 1.0 "" "" 10.1 9.5 0.99 1.7 2.0 "" "" 0.193 +502 288.0 391.7 35.0 1320.0 1.0 "" "" 23.1 9.5 0.99 1.7 2.0 "" "" 0.071 +503 288.0 435.7 35.0 1320.0 1.0 "" "" 11.9 9.5 0.99 1.7 2.0 "" "" 0.336 +504 288.0 232.4 35.0 1320.0 1.0 "" "" 9.7 9.5 0.99 1.7 4.0 "" "" 0.003 +505 288.0 148.3 35.0 1320.0 1.0 "" "" 410.7 9.5 0.99 1.7 3.0 "" "" 0.051 +506 288.0 604.8 35.0 1320.0 1.0 "" "" 10.9 9.5 0.99 1.7 1.0 "" "" 0.845 +507 288.0 367.8 35.0 1320.0 1.0 "" "" 15.0 9.5 0.99 1.7 3.0 "" "" 0.316 +508 288.0 491.2 35.0 1320.0 1.0 "" "" 24.1 9.5 0.99 1.7 0.0 "" "" 0.59 +509 288.0 1014.1 35.0 1320.0 1.0 "" "" 16.1 9.5 0.99 1.7 4.0 "" "" 0.332 +510 288.0 1117.8 35.0 1320.0 1.0 "" "" 11.4 9.5 0.99 1.7 3.0 "" "" 0.924 +511 288.0 472.8 35.0 1320.0 1.0 "" "" 16.1 9.5 0.99 1.7 4.0 "" "" 0.554 +512 288.0 642.9 35.0 1320.0 1.0 "" "" 11.8 9.5 0.99 1.7 2.0 "" "" 0.43 +513 288.0 208.0 35.0 1320.0 1.0 "" "" 17.9 9.5 0.99 1.7 2.0 "" "" 0.152 +514 288.0 783.0 35.0 1320.0 1.0 "" "" 12.7 9.5 0.99 1.7 3.0 "" "" 0.93 +515 288.0 869.7 35.0 1320.0 1.0 "" "" 23.6 9.5 0.99 1.7 2.0 "" "" 0.328 +516 288.0 274.3 35.0 1320.0 1.0 "" "" 13.0 9.5 0.99 1.7 4.0 "" "" 0.25 +517 288.0 458.5 35.0 1320.0 1.0 "" "" 12.5 9.5 0.99 1.7 4.0 "" "" 0.546 +518 288.0 1750.0 35.0 1320.0 1.0 "" "" 10.2 9.5 0.99 1.7 1.0 "" "" 0.991 +519 288.0 1113.0 35.0 1320.0 1.0 "" "" 14.2 9.5 0.99 1.7 2.0 "" "" 1.267 +520 288.0 257.2 35.0 1320.0 1.0 "" "" 13.4 9.5 0.99 1.7 2.0 "" "" 0.002 +521 288.0 1075.0 35.0 1320.0 1.0 "" "" 10.8 9.5 0.99 1.7 3.0 "" "" 0.055 +522 288.0 313.5 35.0 1320.0 1.0 "" "" 13.5 9.5 0.99 1.7 4.0 "" "" 0.077 +523 288.0 527.0 35.0 1320.0 1.0 "" "" 10.9 9.5 0.99 1.7 3.0 "" "" 0.442 +524 288.0 549.7 35.0 1320.0 1.0 "" "" 11.6 9.5 0.99 1.7 1.0 "" "" 0.25 +525 288.0 990.7 35.0 1320.0 1.0 "" "" 60.2 9.5 0.99 1.7 2.0 "" "" 0.987 +526 288.0 446.0 35.0 1320.0 1.0 "" "" 32.2 9.5 0.99 1.7 1.0 "" "" 0.548 +527 288.0 398.9 35.0 1320.0 1.0 "" "" 11.3 9.5 0.99 1.7 3.0 "" "" 0.42 +528 288.0 257.7 35.0 1320.0 1.0 "" "" 42.6 9.5 0.99 1.7 1.0 "" "" 0.206 +529 288.0 413.9 35.0 1320.0 1.0 "" "" 88.2 9.5 0.99 1.7 2.0 "" "" 0.428 +530 288.0 455.6 35.0 1320.0 1.0 "" "" 15.1 9.5 0.99 1.7 4.0 "" "" 0.043 +531 288.0 346.0 35.0 1320.0 1.0 "" "" 268.1 9.5 0.99 1.7 3.0 "" "" 0.136 +532 288.0 587.8 35.0 1320.0 1.0 "" "" 45.8 9.5 0.99 1.7 2.0 "" "" 0.44 +533 288.0 343.5 35.0 1320.0 1.0 "" "" 56.1 9.5 0.99 1.7 3.0 "" "" 0.357 +534 288.0 1243.0 35.0 1320.0 1.0 "" "" 13.7 9.5 0.99 1.7 1.0 "" "" 1.553 +535 288.0 483.7 35.0 1320.0 1.0 "" "" 9.9 9.5 0.99 1.7 3.0 "" "" 0.426 +536 288.0 471.1 35.0 1320.0 1.0 "" "" 17.0 9.5 0.99 1.7 3.0 "" "" 0.429 +537 288.0 269.3 35.0 1320.0 1.0 "" "" 20.6 9.5 0.99 1.7 1.0 "" "" 0.267 +538 288.0 242.4 35.0 1320.0 1.0 "" "" 12.8 9.5 0.99 1.7 3.0 "" "" 0.066 +539 288.0 1627.4 35.0 1320.0 1.0 "" "" 13.3 9.5 0.99 1.7 2.0 "" "" 1.156 +540 288.0 562.4 35.0 1320.0 1.0 "" "" 24.3 9.5 0.99 1.7 2.0 "" "" 0.595 +541 288.0 125.9 35.0 1320.0 1.0 "" "" 11.6 9.5 0.99 1.7 1.0 "" "" 0.025 +542 288.0 1062.3 35.0 1320.0 1.0 "" "" 31.0 9.5 0.99 1.7 3.0 "" "" 1.306 +543 288.0 366.7 35.0 1320.0 1.0 "" "" 13.0 9.5 0.99 1.7 2.0 "" "" 0.433 +544 288.0 814.9 35.0 1320.0 1.0 "" "" 13.7 9.5 0.99 1.7 3.0 "" "" 0.752 +545 288.0 124.3 35.0 1320.0 1.0 "" "" 14.8 9.5 0.99 1.7 4.0 "" "" 0.046 +546 288.0 431.5 35.0 1320.0 1.0 "" "" 12.5 9.5 0.99 1.7 2.0 "" "" 0.041 +547 288.0 312.5 35.0 1320.0 1.0 "" "" 18.6 9.5 0.99 1.7 1.0 "" "" 0.17 +548 288.0 720.7 35.0 1320.0 1.0 "" "" 26.7 9.5 0.99 1.7 2.0 "" "" 0.329 +549 288.0 537.4 35.0 1320.0 1.0 "" "" 23.5 9.5 0.99 1.7 2.0 "" "" 0.321 +550 288.0 437.0 35.0 1320.0 1.0 "" "" 11.9 9.5 0.99 1.7 3.0 "" "" 0.403 +551 288.0 443.4 35.0 1320.0 1.0 "" "" 28.3 9.5 0.99 1.7 2.0 "" "" 0.39 +552 288.0 585.0 35.0 1320.0 1.0 "" "" 19.9 9.5 0.99 1.7 1.0 "" "" 0.368 +553 288.0 811.8 35.0 1320.0 1.0 "" "" 16.2 9.5 0.99 1.7 3.0 "" "" 0.789 +554 288.0 608.4 35.0 1320.0 1.0 "" "" 21.8 9.5 0.99 1.7 2.0 "" "" 0.389 +555 288.0 281.2 35.0 1320.0 1.0 "" "" 17.0 9.5 0.99 1.7 3.0 "" "" 0.099 +556 288.0 594.1 35.0 1320.0 1.0 "" "" 13.5 9.5 0.99 1.7 4.0 "" "" 0.145 +557 288.0 329.8 35.0 1320.0 1.0 "" "" 33.1 9.5 0.99 1.7 3.0 "" "" 0.282 +558 288.0 593.5 35.0 1320.0 1.0 "" "" 61.8 9.5 0.99 1.7 1.0 "" "" 0.7 +559 288.0 1161.1 35.0 1320.0 1.0 "" "" 12.8 9.5 0.99 1.7 2.0 "" "" 0.899 +560 288.0 290.6 35.0 1320.0 1.0 "" "" 29.3 9.5 0.99 1.7 2.0 "" "" 0.026 +561 288.0 416.8 35.0 1320.0 1.0 "" "" 25.7 9.5 0.99 1.7 3.0 "" "" 0.419 +562 288.0 1513.6 35.0 1320.0 1.0 "" "" 16.6 9.5 0.99 1.7 3.0 "" "" 0.275 +563 288.0 408.6 35.0 1320.0 1.0 "" "" 41.7 9.5 0.99 1.7 2.0 "" "" 0.386 +564 288.0 1315.3 35.0 1320.0 1.0 "" "" 36.9 9.5 0.99 1.7 3.0 "" "" 1.159 +565 288.0 446.2 35.0 1320.0 1.0 "" "" 13.4 9.5 0.99 1.7 1.0 "" "" 0.158 +566 288.0 298.4 35.0 1320.0 1.0 "" "" 9.7 9.5 0.99 1.7 2.0 "" "" 0.207 +567 288.0 721.1 35.0 1320.0 1.0 "" "" 67.3 9.5 0.99 1.7 2.0 "" "" 0.351 +568 288.0 184.3 35.0 1320.0 1.0 "" "" 171.1 9.5 0.99 1.7 2.0 "" "" 0.036 +569 288.0 129.6 35.0 1320.0 1.0 "" "" 10.8 9.5 0.99 1.7 1.0 "" "" 0.097 +570 288.0 624.8 35.0 1320.0 1.0 "" "" 132.1 9.5 0.99 1.7 2.0 "" "" 0.15 +571 288.0 1829.0 35.0 1320.0 1.0 "" "" 22.9 9.5 0.99 1.7 3.0 "" "" 1.818 +572 288.0 438.5 35.0 1320.0 1.0 "" "" 183.6 9.5 0.99 1.7 3.0 "" "" 0.205 +573 288.0 1390.3 35.0 1320.0 1.0 "" "" 16.4 9.5 0.99 1.7 1.0 "" "" 1.05 +574 288.0 829.0 35.0 1320.0 1.0 "" "" 20.2 9.5 0.99 1.7 4.0 "" "" 0.253 +575 288.0 771.9 35.0 1320.0 1.0 "" "" 11.6 9.5 0.99 1.7 1.0 "" "" 0.675 +576 288.0 575.5 35.0 1320.0 1.0 "" "" 40.1 9.5 0.99 1.7 3.0 "" "" 0.693 +577 288.0 282.4 35.0 1320.0 1.0 "" "" 17.0 9.5 0.99 1.7 2.0 "" "" 0.14 +578 288.0 166.3 35.0 1320.0 1.0 "" "" 11.0 9.5 0.99 1.7 3.0 "" "" 0.18 +579 288.0 390.1 35.0 1320.0 1.0 "" "" 9.7 9.5 0.99 1.7 1.0 "" "" 0.124 +580 288.0 518.5 35.0 1320.0 1.0 "" "" 67.6 9.5 0.99 1.7 2.0 "" "" 0.476 +581 288.0 485.0 35.0 1320.0 1.0 "" "" 14.9 9.5 0.99 1.7 5.0 "" "" 0.047 +582 288.0 1440.3 35.0 1320.0 1.0 "" "" 18.3 9.5 0.99 1.7 2.0 "" "" 1.788 +583 288.0 648.5 35.0 1320.0 1.0 "" "" 21.9 9.5 0.99 1.7 2.0 "" "" 0.519 +584 288.0 732.4 35.0 1320.0 1.0 "" "" 14.0 9.5 0.99 1.7 3.0 "" "" 0.514 +585 288.0 498.6 35.0 1320.0 1.0 "" "" 15.4 9.5 0.99 1.7 2.0 "" "" 0.257 +586 288.0 1418.2 35.0 1320.0 1.0 "" "" 11.6 9.5 0.99 1.7 4.0 "" "" 0.02 +587 288.0 510.3 35.0 1320.0 1.0 "" "" 12.5 9.5 0.99 1.7 1.0 "" "" 0.508 +588 288.0 641.0 35.0 1320.0 1.0 "" "" 58.8 9.5 0.99 1.7 4.0 "" "" 0.536 +589 288.0 176.0 35.0 1320.0 1.0 "" "" 9.8 9.5 0.99 1.7 3.0 "" "" 0.032 +590 288.0 515.8 35.0 1320.0 1.0 "" "" 13.9 9.5 0.99 1.7 1.0 "" "" 0.441 +591 288.0 131.3 35.0 1320.0 1.0 "" "" 151.3 9.5 0.99 1.7 1.0 "" "" 0.04 +592 288.0 437.2 35.0 1320.0 1.0 "" "" 62.6 9.5 0.99 1.7 2.0 "" "" 0.449 +593 288.0 175.1 35.0 1320.0 1.0 "" "" 289.1 9.5 0.99 1.7 2.0 "" "" 0.151 +594 288.0 702.0 35.0 1320.0 1.0 "" "" 12.5 9.5 0.99 1.7 3.0 "" "" 0.526 +595 288.0 1701.6 35.0 1320.0 1.0 "" "" 12.3 9.5 0.99 1.7 2.0 "" "" 1.675 +596 288.0 459.8 35.0 1320.0 1.0 "" "" 13.7 9.5 0.99 1.7 2.0 "" "" 0.572 +597 288.0 2375.3 35.0 1320.0 1.0 "" "" 9.7 9.5 0.99 1.7 3.0 "" "" 2.793 +598 288.0 445.5 35.0 1320.0 1.0 "" "" 17.7 9.5 0.99 1.7 2.0 "" "" 0.209 +599 288.0 199.4 35.0 1320.0 1.0 "" "" 20.1 9.5 0.99 1.7 2.0 "" "" 0.122 +600 288.0 1155.0 35.0 1320.0 1.0 "" "" 16.3 9.5 0.99 1.7 2.0 "" "" 0.493 +601 288.0 292.6 35.0 1320.0 1.0 "" "" 27.7 9.5 0.99 1.7 3.0 "" "" 0.101 +602 288.0 410.7 35.0 1320.0 1.0 "" "" 10.8 9.5 0.99 1.7 2.0 "" "" 0.242 +603 288.0 192.1 35.0 1320.0 1.0 "" "" 18.4 9.5 0.99 1.7 4.0 "" "" 0.126 +604 288.0 373.7 35.0 1320.0 1.0 "" "" 9.9 9.5 0.99 1.7 3.0 "" "" 0.031 +605 288.0 722.7 35.0 1320.0 1.0 "" "" 13.5 9.5 0.99 1.7 5.0 "" "" 0.574 +606 288.0 915.4 35.0 1320.0 1.0 "" "" 12.4 9.5 0.99 1.7 3.0 "" "" 0.87 +607 288.0 343.0 35.0 1320.0 1.0 "" "" 13.8 9.5 0.99 1.7 3.0 "" "" 0.287 +608 288.0 127.5 35.0 1320.0 1.0 "" "" 15.8 9.5 0.99 1.7 2.0 "" "" 0.055 +609 288.0 771.3 35.0 1320.0 1.0 "" "" 23.1 9.5 0.99 1.7 3.0 "" "" 0.987 +610 288.0 506.2 35.0 1320.0 1.0 "" "" 18.4 9.5 0.99 1.7 3.0 "" "" 0.501 +611 288.0 607.9 35.0 1320.0 1.0 "" "" 27.8 9.5 0.99 1.7 2.0 "" "" 0.242 +612 288.0 975.2 35.0 1320.0 1.0 "" "" 34.8 9.5 0.99 1.7 1.0 "" "" 0.981 +613 288.0 592.0 35.0 1320.0 1.0 "" "" 10.5 9.5 0.99 1.7 2.0 "" "" 0.018 +614 288.0 653.4 35.0 1320.0 1.0 "" "" 25.1 9.5 0.99 1.7 2.0 "" "" 0.476 +615 288.0 843.6 35.0 1320.0 1.0 "" "" 10.4 9.5 0.99 1.7 3.0 "" "" 1.086 +616 288.0 348.3 35.0 1320.0 1.0 "" "" 10.1 9.5 0.99 1.7 3.0 "" "" 0.181 +617 288.0 185.9 35.0 1320.0 1.0 "" "" 24.8 9.5 0.99 1.7 3.0 "" "" 0.118 +618 288.0 1906.7 35.0 1320.0 1.0 "" "" 13.0 9.5 0.99 1.7 2.0 "" "" 1.795 +619 288.0 237.6 35.0 1320.0 1.0 "" "" 27.8 9.5 0.99 1.7 1.0 "" "" 0.184 +620 288.0 267.9 35.0 1320.0 1.0 "" "" 58.0 9.5 0.99 1.7 3.0 "" "" 0.116 +621 288.0 406.6 35.0 1320.0 1.0 "" "" 15.0 9.5 0.99 1.7 3.0 "" "" 0.415 +622 288.0 430.5 35.0 1320.0 1.0 "" "" 12.6 9.5 0.99 1.7 3.0 "" "" 0.455 +623 288.0 1150.5 35.0 1320.0 1.0 "" "" 16.7 9.5 0.99 1.7 3.0 "" "" 0.369 +624 288.0 383.2 35.0 1320.0 1.0 "" "" 15.8 9.5 0.99 1.7 3.0 "" "" 0.175 +625 288.0 671.0 35.0 1320.0 1.0 "" "" 20.1 9.5 0.99 1.7 3.0 "" "" 0.894 +626 288.0 372.8 35.0 1320.0 1.0 "" "" 14.0 9.5 0.99 1.7 2.0 "" "" 0.102 +627 288.0 888.9 35.0 1320.0 1.0 "" "" 12.9 9.5 0.99 1.7 3.0 "" "" 0.032 +628 288.0 1052.2 35.0 1320.0 1.0 "" "" 26.5 9.5 0.99 1.7 2.0 "" "" 1.371 +629 288.0 1023.3 35.0 1320.0 1.0 "" "" 21.5 9.5 0.99 1.7 1.0 "" "" 1.418 +630 288.0 281.3 35.0 1320.0 1.0 "" "" 25.2 9.5 0.99 1.7 4.0 "" "" 0.041 +631 288.0 238.1 35.0 1320.0 1.0 "" "" 12.6 9.5 0.99 1.7 2.0 "" "" 0.251 +632 288.0 552.2 35.0 1320.0 1.0 "" "" 11.6 9.5 0.99 1.7 3.0 "" "" 0.562 +633 288.0 558.2 35.0 1320.0 1.0 "" "" 9.8 9.5 0.99 1.7 3.0 "" "" 0.484 +634 288.0 337.6 35.0 1320.0 1.0 "" "" 22.0 9.5 0.99 1.7 1.0 "" "" 0.08 +635 288.0 295.2 35.0 1320.0 1.0 "" "" 11.4 9.5 0.99 1.7 3.0 "" "" 0.209 +636 288.0 575.8 35.0 1320.0 1.0 "" "" 10.4 9.5 0.99 1.7 2.0 "" "" 0.189 +637 288.0 375.9 35.0 1320.0 1.0 "" "" 14.7 9.5 0.99 1.7 1.0 "" "" 0.064 +638 288.0 129.2 35.0 1320.0 1.0 "" "" 24.3 9.5 0.99 1.7 2.0 "" "" 0.073 +639 288.0 198.3 35.0 1320.0 1.0 "" "" 10.5 9.5 0.99 1.7 0.0 "" "" 0.058 +640 288.0 445.5 35.0 1320.0 1.0 "" "" 87.9 9.5 0.99 1.7 1.0 "" "" 0.33 +641 288.0 215.9 35.0 1320.0 1.0 "" "" 35.2 9.5 0.99 1.7 1.0 "" "" 0.114 +642 288.0 212.4 35.0 1320.0 1.0 "" "" 16.9 9.5 0.99 1.7 3.0 "" "" 0.084 +643 288.0 1228.4 35.0 1320.0 1.0 "" "" 13.0 9.5 0.99 1.7 3.0 "" "" 1.305 +644 288.0 578.5 35.0 1320.0 1.0 "" "" 11.5 9.5 0.99 1.7 1.0 "" "" 0.149 +645 288.0 706.3 35.0 1320.0 1.0 "" "" 39.2 9.5 0.99 1.7 1.0 "" "" 0.794 +646 288.0 597.8 35.0 1320.0 1.0 "" "" 38.6 9.5 0.99 1.7 3.0 "" "" 0.536 +647 288.0 283.6 35.0 1320.0 1.0 "" "" 14.3 9.5 0.99 1.7 2.0 "" "" 0.195 +648 288.0 2236.7 35.0 1320.0 1.0 "" "" 20.4 9.5 0.99 1.7 2.0 "" "" 1.327 +649 288.0 694.4 35.0 1320.0 1.0 "" "" 15.4 9.5 0.99 1.7 4.0 "" "" 0.099 +650 288.0 213.5 35.0 1320.0 1.0 "" "" 11.9 9.5 0.99 1.7 3.0 "" "" 0.136 +651 288.0 961.6 35.0 1320.0 1.0 "" "" 28.1 9.5 0.99 1.7 2.0 "" "" 0.475 +652 288.0 801.4 35.0 1320.0 1.0 "" "" 12.3 9.5 0.99 1.7 2.0 "" "" 0.818 +653 288.0 396.1 35.0 1320.0 1.0 "" "" 22.6 9.5 0.99 1.7 4.0 "" "" 0.233 +654 288.0 1093.6 35.0 1320.0 1.0 "" "" 10.9 9.5 0.99 1.7 3.0 "" "" 0.806 +655 288.0 493.6 35.0 1320.0 1.0 "" "" 9.9 9.5 0.99 1.7 1.0 "" "" 0.322 +656 288.0 518.1 35.0 1320.0 1.0 "" "" 14.7 9.5 0.99 1.7 3.0 "" "" 0.52 +657 288.0 478.0 35.0 1320.0 1.0 "" "" 16.7 9.5 0.99 1.7 4.0 "" "" 0.094 +658 288.0 242.1 35.0 1320.0 1.0 "" "" 139.7 9.5 0.99 1.7 3.0 "" "" 0.098 +659 288.0 261.7 35.0 1320.0 1.0 "" "" 43.3 9.5 0.99 1.7 3.0 "" "" 0.142 +660 288.0 443.3 35.0 1320.0 1.0 "" "" 11.6 9.5 0.99 1.7 2.0 "" "" 0.042 +661 288.0 1386.8 35.0 1320.0 1.0 "" "" 26.7 9.5 0.99 1.7 3.0 "" "" 0.544 +662 288.0 475.0 35.0 1320.0 1.0 "" "" 22.0 9.5 0.99 1.7 1.0 "" "" 0.228 +663 288.0 545.5 35.0 1320.0 1.0 "" "" 9.7 9.5 0.99 1.7 3.0 "" "" 0.709 +664 288.0 220.1 35.0 1320.0 1.0 "" "" 21.6 9.5 0.99 1.7 2.0 "" "" 0.093 +665 288.0 809.9 35.0 1320.0 1.0 "" "" 12.2 9.5 0.99 1.7 2.0 "" "" 0.829 +666 288.0 358.1 35.0 1320.0 1.0 "" "" 14.5 9.5 0.99 1.7 1.0 "" "" 0.122 +667 288.0 842.0 35.0 1320.0 1.0 "" "" 12.4 9.5 0.99 1.7 3.0 "" "" 0.323 +668 288.0 1395.1 35.0 1320.0 1.0 "" "" 11.5 9.5 0.99 1.7 4.0 "" "" 0.441 +669 288.0 732.5 35.0 1320.0 1.0 "" "" 42.3 9.5 0.99 1.7 3.0 "" "" 0.612 +670 288.0 423.2 35.0 1320.0 1.0 "" "" 15.5 9.5 0.99 1.7 2.0 "" "" 0.322 +671 288.0 1328.1 35.0 1320.0 1.0 "" "" 10.8 9.5 0.99 1.7 1.0 "" "" 0.927 +672 288.0 530.2 35.0 1320.0 1.0 "" "" 13.2 9.5 0.99 1.7 2.0 "" "" 0.49 +673 288.0 1584.2 35.0 1320.0 1.0 "" "" 15.0 9.5 0.99 1.7 3.0 "" "" 1.705 +674 288.0 147.1 35.0 1320.0 1.0 "" "" 15.8 9.5 0.99 1.7 2.0 "" "" 0.047 +675 288.0 726.1 35.0 1320.0 1.0 "" "" 16.9 9.5 0.99 1.7 3.0 "" "" 0.903 +676 288.0 346.6 35.0 1320.0 1.0 "" "" 10.1 9.5 0.99 1.7 4.0 "" "" 0.028 +677 288.0 1399.0 35.0 1320.0 1.0 "" "" 15.0 9.5 0.99 1.7 3.0 "" "" 1.447 +678 288.0 884.5 35.0 1320.0 1.0 "" "" 9.8 9.5 0.99 1.7 3.0 "" "" 1.112 +679 288.0 1032.9 35.0 1320.0 1.0 "" "" 23.1 9.5 0.99 1.7 2.0 "" "" 1.183 +680 288.0 182.6 35.0 1320.0 1.0 "" "" 12.3 9.5 0.99 1.7 1.0 "" "" 0.056 +681 288.0 1051.2 35.0 1320.0 1.0 "" "" 10.4 9.5 0.99 1.7 2.0 "" "" 1.131 +682 288.0 1485.1 35.0 1320.0 1.0 "" "" 13.5 9.5 0.99 1.7 3.0 "" "" 1.608 +683 288.0 299.7 35.0 1320.0 1.0 "" "" 17.1 9.5 0.99 1.7 5.0 "" "" 0.15 +684 288.0 395.2 35.0 1320.0 1.0 "" "" 13.2 9.5 0.99 1.7 3.0 "" "" 0.229 +685 288.0 517.8 35.0 1320.0 1.0 "" "" 23.0 9.5 0.99 1.7 1.0 "" "" 0.276 +686 288.0 356.1 35.0 1320.0 1.0 "" "" 18.7 9.5 0.99 1.7 3.0 "" "" 0.325 +687 288.0 368.5 35.0 1320.0 1.0 "" "" 54.5 9.5 0.99 1.7 1.0 "" "" 0.25 +688 288.0 277.4 35.0 1320.0 1.0 "" "" 18.4 9.5 0.99 1.7 2.0 "" "" 0.303 +689 288.0 644.6 35.0 1320.0 1.0 "" "" 10.1 9.5 0.99 1.7 2.0 "" "" 0.764 +690 288.0 1221.8 35.0 1320.0 1.0 "" "" 10.1 9.5 0.99 1.7 4.0 "" "" 0.005 +691 288.0 422.4 35.0 1320.0 1.0 "" "" 14.3 9.5 0.99 1.7 3.0 "" "" 0.321 +692 288.0 374.0 35.0 1320.0 1.0 "" "" 53.2 9.5 0.99 1.7 1.0 "" "" 0.4 +693 288.0 422.3 35.0 1320.0 1.0 "" "" 42.6 9.5 0.99 1.7 3.0 "" "" 0.245 +694 288.0 283.2 35.0 1320.0 1.0 "" "" 15.3 9.5 0.99 1.7 2.0 "" "" 0.327 +695 288.0 1116.8 35.0 1320.0 1.0 "" "" 39.6 9.5 0.99 1.7 4.0 "" "" 0.638 +696 288.0 408.3 35.0 1320.0 1.0 "" "" 10.9 9.5 0.99 1.7 2.0 "" "" 0.353 +697 288.0 296.8 35.0 1320.0 1.0 "" "" 30.9 9.5 0.99 1.7 4.0 "" "" 0.026 +698 288.0 294.1 35.0 1320.0 1.0 "" "" 22.3 9.5 0.99 1.7 2.0 "" "" 0.119 +699 288.0 385.7 35.0 1320.0 1.0 "" "" 68.5 9.5 0.99 1.7 2.0 "" "" 0.272 +700 288.0 351.1 35.0 1320.0 1.0 "" "" 15.3 9.5 0.99 1.7 1.0 "" "" 0.216 +701 288.0 189.6 35.0 1320.0 1.0 "" "" 13.8 9.5 0.99 1.7 2.0 "" "" 0.042 +702 288.0 180.7 35.0 1320.0 1.0 "" "" 11.0 9.5 0.99 1.7 2.0 "" "" 0.136 +703 288.0 653.5 35.0 1320.0 1.0 "" "" 11.2 9.5 0.99 1.7 4.0 "" "" 0.285 +704 288.0 779.7 35.0 1320.0 1.0 "" "" 11.9 9.5 0.99 1.7 2.0 "" "" 1.103 +705 288.0 217.9 35.0 1320.0 1.0 "" "" 15.9 9.5 0.99 1.7 1.0 "" "" 0.018 +706 288.0 185.0 35.0 1320.0 1.0 "" "" 34.9 9.5 0.99 1.7 2.0 "" "" 0.092 +707 288.0 439.6 35.0 1320.0 1.0 "" "" 41.0 9.5 0.99 1.7 3.0 "" "" 0.427 +708 288.0 623.7 35.0 1320.0 1.0 "" "" 9.7 9.5 0.99 1.7 3.0 "" "" 0.238 +709 288.0 450.0 35.0 1320.0 1.0 "" "" 11.7 9.5 0.99 1.7 3.0 "" "" 0.267 +710 288.0 893.3 35.0 1320.0 1.0 "" "" 10.0 9.5 0.99 1.7 3.0 "" "" 1.138 +711 288.0 414.6 35.0 1320.0 1.0 "" "" 37.3 9.5 0.99 1.7 3.0 "" "" 0.323 +712 288.0 851.4 35.0 1320.0 1.0 "" "" 12.6 9.5 0.99 1.7 3.0 "" "" 0.832 +713 288.0 214.4 35.0 1320.0 1.0 "" "" 12.4 9.5 0.99 1.7 2.0 "" "" 0.069 +714 288.0 611.7 35.0 1320.0 1.0 "" "" 10.6 9.5 0.99 1.7 2.0 "" "" 0.485 +715 288.0 301.7 35.0 1320.0 1.0 "" "" 21.8 9.5 0.99 1.7 2.0 "" "" 0.112 +716 288.0 761.3 35.0 1320.0 1.0 "" "" 11.8 9.5 0.99 1.7 1.0 "" "" 0.537 +717 288.0 309.8 35.0 1320.0 1.0 "" "" 14.8 9.5 0.99 1.7 2.0 "" "" 0.138 +718 288.0 697.6 35.0 1320.0 1.0 "" "" 35.4 9.5 0.99 1.7 1.0 "" "" 0.334 +719 288.0 185.1 35.0 1320.0 1.0 "" "" 11.1 9.5 0.99 1.7 3.0 "" "" 0.131 +720 288.0 1456.7 35.0 1320.0 1.0 "" "" 11.3 9.5 0.99 1.7 1.0 "" "" 1.606 +721 288.0 138.2 35.0 1320.0 1.0 "" "" 9.5 9.5 0.99 1.7 1.0 "" "" 0.032 +722 288.0 163.7 35.0 1320.0 1.0 "" "" 50.7 9.5 0.99 1.7 1.0 "" "" 0.034 +723 288.0 308.7 35.0 1320.0 1.0 "" "" 29.4 9.5 0.99 1.7 2.0 "" "" 0.345 +724 288.0 582.7 35.0 1320.0 1.0 "" "" 9.5 9.5 0.99 1.7 3.0 "" "" 0.376 +725 288.0 771.6 35.0 1320.0 1.0 "" "" 16.4 9.5 0.99 1.7 1.0 "" "" 0.037 +726 288.0 483.9 35.0 1320.0 1.0 "" "" 21.6 9.5 0.99 1.7 3.0 "" "" 0.387 +727 288.0 951.5 35.0 1320.0 1.0 "" "" 11.3 9.5 0.99 1.7 4.0 "" "" 0.402 +728 288.0 225.3 35.0 1320.0 1.0 "" "" 12.7 9.5 0.99 1.7 1.0 "" "" 0.049 +729 288.0 561.8 35.0 1320.0 1.0 "" "" 10.1 9.5 0.99 1.7 3.0 "" "" 0.32 +730 288.0 603.7 35.0 1320.0 1.0 "" "" 10.6 9.5 0.99 1.7 2.0 "" "" 0.743 +731 288.0 1044.5 35.0 1320.0 1.0 "" "" 15.8 9.5 0.99 1.7 3.0 "" "" 0.675 +732 288.0 409.1 35.0 1320.0 1.0 "" "" 13.4 9.5 0.99 1.7 2.0 "" "" 0.256 +733 288.0 341.3 35.0 1320.0 1.0 "" "" 9.9 9.5 0.99 1.7 3.0 "" "" 0.098 +734 288.0 1638.5 35.0 1320.0 1.0 "" "" 88.3 9.5 0.99 1.7 2.0 "" "" 0.205 +735 288.0 443.9 35.0 1320.0 1.0 "" "" 49.0 9.5 0.99 1.7 4.0 "" "" 0.132 +736 288.0 490.6 35.0 1320.0 1.0 "" "" 18.0 9.5 0.99 1.7 2.0 "" "" 0.501 +737 288.0 420.7 35.0 1320.0 1.0 "" "" 18.0 9.5 0.99 1.7 3.0 "" "" 0.119 +738 288.0 686.3 35.0 1320.0 1.0 "" "" 15.7 9.5 0.99 1.7 3.0 "" "" 0.417 +739 288.0 767.1 35.0 1320.0 1.0 "" "" 10.6 9.5 0.99 1.7 1.0 "" "" 0.48 +740 288.0 475.7 35.0 1320.0 1.0 "" "" 9.8 9.5 0.99 1.7 3.0 "" "" 0.527 +741 288.0 1537.6 35.0 1320.0 1.0 "" "" 11.3 9.5 0.99 1.7 3.0 "" "" 0.862 +742 288.0 195.3 35.0 1320.0 1.0 "" "" 9.9 9.5 0.99 1.7 2.0 "" "" 0.168 +743 288.0 619.2 35.0 1320.0 1.0 "" "" 39.1 9.5 0.99 1.7 3.0 "" "" 0.673 +744 288.0 162.7 35.0 1320.0 1.0 "" "" 62.5 9.5 0.99 1.7 3.0 "" "" 0.04 +745 288.0 1006.0 35.0 1320.0 1.0 "" "" 16.0 9.5 0.99 1.7 4.0 "" "" 0.137 +746 288.0 358.5 35.0 1320.0 1.0 "" "" 112.9 9.5 0.99 1.7 2.0 "" "" 0.096 +747 288.0 560.2 35.0 1320.0 1.0 "" "" 10.7 9.5 0.99 1.7 4.0 "" "" 0.201 +748 288.0 690.6 35.0 1320.0 1.0 "" "" 25.1 9.5 0.99 1.7 4.0 "" "" 0.668 +749 288.0 654.1 35.0 1320.0 1.0 "" "" 9.9 9.5 0.99 1.7 2.0 "" "" 0.565 +750 288.0 692.5 35.0 1320.0 1.0 "" "" 16.8 9.5 0.99 1.7 1.0 "" "" 0.083 +751 288.0 396.5 35.0 1320.0 1.0 "" "" 29.3 9.5 0.99 1.7 4.0 "" "" 0.229 +752 288.0 223.3 35.0 1320.0 1.0 "" "" 78.2 9.5 0.99 1.7 2.0 "" "" 0.12 +753 288.0 483.5 35.0 1320.0 1.0 "" "" 10.1 9.5 0.99 1.7 3.0 "" "" 0.427 +754 288.0 937.4 35.0 1320.0 1.0 "" "" 29.9 9.5 0.99 1.7 2.0 "" "" 1.326 +755 288.0 188.5 35.0 1320.0 1.0 "" "" 18.9 9.5 0.99 1.7 2.0 "" "" 0.138 +756 288.0 529.8 35.0 1320.0 1.0 "" "" 12.6 9.5 0.99 1.7 1.0 "" "" 0.512 +757 288.0 196.3 35.0 1320.0 1.0 "" "" 12.2 9.5 0.99 1.7 3.0 "" "" 0.177 +758 288.0 489.6 35.0 1320.0 1.0 "" "" 159.3 9.5 0.99 1.7 1.0 "" "" 0.501 +759 288.0 527.5 35.0 1320.0 1.0 "" "" 25.6 9.5 0.99 1.7 4.0 "" "" 0.202 +760 288.0 544.7 35.0 1320.0 1.0 "" "" 22.5 9.5 0.99 1.7 3.0 "" "" 0.613 +761 288.0 306.1 35.0 1320.0 1.0 "" "" 13.8 9.5 0.99 1.7 1.0 "" "" 0.292 +762 288.0 650.9 35.0 1320.0 1.0 "" "" 50.7 9.5 0.99 1.7 3.0 "" "" 0.731 +763 288.0 478.2 35.0 1320.0 1.0 "" "" 10.7 9.5 0.99 1.7 5.0 "" "" 0.052 +764 288.0 190.9 35.0 1320.0 1.0 "" "" 9.8 9.5 0.99 1.7 4.0 "" "" 0.113 +765 288.0 1307.3 35.0 1320.0 1.0 "" "" 20.9 9.5 0.99 1.7 2.0 "" "" 1.913 +766 288.0 1313.0 35.0 1320.0 1.0 "" "" 23.7 9.5 0.99 1.7 3.0 "" "" 0.787 +767 288.0 408.8 35.0 1320.0 1.0 "" "" 78.2 9.5 0.99 1.7 5.0 "" "" 0.145 +768 288.0 831.6 35.0 1320.0 1.0 "" "" 9.9 9.5 0.99 1.7 3.0 "" "" 0.165 +769 288.0 664.2 35.0 1320.0 1.0 "" "" 11.4 9.5 0.99 1.7 1.0 "" "" 0.595 +770 288.0 259.1 35.0 1320.0 1.0 "" "" 120.2 9.5 0.99 1.7 4.0 "" "" 0.093 +771 288.0 310.4 35.0 1320.0 1.0 "" "" 9.7 9.5 0.99 1.7 4.0 "" "" 0.199 +772 288.0 962.8 35.0 1320.0 1.0 "" "" 11.5 9.5 0.99 1.7 4.0 "" "" 0.919 +773 288.0 830.4 35.0 1320.0 1.0 "" "" 9.9 9.5 0.99 1.7 3.0 "" "" 1.007 +774 288.0 855.0 35.0 1320.0 1.0 "" "" 27.6 9.5 0.99 1.7 4.0 "" "" 0.619 +775 288.0 324.6 35.0 1320.0 1.0 "" "" 127.6 9.5 0.99 1.7 3.0 "" "" 0.248 +776 288.0 596.0 35.0 1320.0 1.0 "" "" 20.0 9.5 0.99 1.7 3.0 "" "" 0.5 +777 288.0 533.7 35.0 1320.0 1.0 "" "" 18.4 9.5 0.99 1.7 2.0 "" "" 0.304 +778 288.0 1653.2 35.0 1320.0 1.0 "" "" 10.7 9.5 0.99 1.7 1.0 "" "" 0.729 +779 288.0 662.3 35.0 1320.0 1.0 "" "" 11.8 9.5 0.99 1.7 3.0 "" "" 0.807 +780 288.0 410.8 35.0 1320.0 1.0 "" "" 72.7 9.5 0.99 1.7 3.0 "" "" 0.014 +781 288.0 856.2 35.0 1320.0 1.0 "" "" 18.5 9.5 0.99 1.7 2.0 "" "" 0.959 +782 288.0 364.5 35.0 1320.0 1.0 "" "" 34.6 9.5 0.99 1.7 1.0 "" "" 0.222 +783 288.0 486.6 35.0 1320.0 1.0 "" "" 10.2 9.5 0.99 1.7 3.0 "" "" 0.508 +784 288.0 362.0 35.0 1320.0 1.0 "" "" 27.8 9.5 0.99 1.7 3.0 "" "" 0.193 +785 288.0 1218.0 35.0 1320.0 1.0 "" "" 19.0 9.5 0.99 1.7 2.0 "" "" 1.56 +786 288.0 1109.0 35.0 1320.0 1.0 "" "" 24.5 9.5 0.99 1.7 4.0 "" "" 1.042 +787 288.0 1330.3 35.0 1320.0 1.0 "" "" 11.2 9.5 0.99 1.7 2.0 "" "" 1.535 +788 288.0 834.8 35.0 1320.0 1.0 "" "" 13.6 9.5 0.99 1.7 4.0 "" "" 0.782 +789 288.0 181.3 35.0 1320.0 1.0 "" "" 34.9 9.5 0.99 1.7 4.0 "" "" 0.073 +790 288.0 538.9 35.0 1320.0 1.0 "" "" 24.6 9.5 0.99 1.7 2.0 "" "" 0.439 +791 288.0 492.1 35.0 1320.0 1.0 "" "" 14.2 9.5 0.99 1.7 2.0 "" "" 0.527 +792 288.0 176.4 35.0 1320.0 1.0 "" "" 16.5 9.5 0.99 1.7 4.0 "" "" 0.106 +793 288.0 427.2 35.0 1320.0 1.0 "" "" 13.3 9.5 0.99 1.7 3.0 "" "" 0.02 +794 288.0 307.4 35.0 1320.0 1.0 "" "" 31.0 9.5 0.99 1.7 2.0 "" "" 0.06 +795 288.0 210.7 35.0 1320.0 1.0 "" "" 12.4 9.5 0.99 1.7 2.0 "" "" 0.015 +796 288.0 1031.6 35.0 1320.0 1.0 "" "" 12.8 9.5 0.99 1.7 3.0 "" "" 0.079 +797 288.0 1439.8 35.0 1320.0 1.0 "" "" 390.0 9.5 0.99 1.7 2.0 "" "" 0.276 +798 288.0 205.5 35.0 1320.0 1.0 "" "" 424.9 9.5 0.99 1.7 2.0 "" "" 0.044 +799 288.0 335.2 35.0 1320.0 1.0 "" "" 81.3 9.5 0.99 1.7 3.0 "" "" 0.224 +800 288.0 737.1 35.0 1320.0 1.0 "" "" 11.7 9.5 0.99 1.7 1.0 "" "" 0.738 +801 288.0 657.5 35.0 1320.0 1.0 "" "" 10.5 9.5 0.99 1.7 4.0 "" "" 0.689 +802 288.0 325.0 35.0 1320.0 1.0 "" "" 17.6 9.5 0.99 1.7 1.0 "" "" 0.197 +803 288.0 360.9 35.0 1320.0 1.0 "" "" 18.9 9.5 0.99 1.7 3.0 "" "" 0.406 +804 288.0 1518.7 35.0 1320.0 1.0 "" "" 10.4 9.5 0.99 1.7 2.0 "" "" 0.719 +805 288.0 650.4 35.0 1320.0 1.0 "" "" 11.0 9.5 0.99 1.7 3.0 "" "" 0.628 +806 288.0 310.7 35.0 1320.0 1.0 "" "" 12.4 9.5 0.99 1.7 3.0 "" "" 0.286 +807 288.0 318.1 35.0 1320.0 1.0 "" "" 43.1 9.5 0.99 1.7 2.0 "" "" 0.159 +808 288.0 422.6 35.0 1320.0 1.0 "" "" 11.2 9.5 0.99 1.7 2.0 "" "" 0.476 +809 288.0 243.3 35.0 1320.0 1.0 "" "" 13.8 9.5 0.99 1.7 4.0 "" "" 0.16 +810 288.0 1414.7 35.0 1320.0 1.0 "" "" 21.0 9.5 0.99 1.7 3.0 "" "" 0.566 +811 288.0 672.1 35.0 1320.0 1.0 "" "" 12.7 9.5 0.99 1.7 3.0 "" "" 0.831 +812 288.0 743.0 35.0 1320.0 1.0 "" "" 19.6 9.5 0.99 1.7 2.0 "" "" 0.912 +813 288.0 906.6 35.0 1320.0 1.0 "" "" 10.3 9.5 0.99 1.7 3.0 "" "" 0.057 +814 288.0 539.5 35.0 1320.0 1.0 "" "" 68.4 9.5 0.99 1.7 2.0 "" "" 0.549 +815 288.0 497.4 35.0 1320.0 1.0 "" "" 66.2 9.5 0.99 1.7 4.0 "" "" 0.248 +816 288.0 861.2 35.0 1320.0 1.0 "" "" 10.9 9.5 0.99 1.7 2.0 "" "" 0.986 +817 288.0 184.9 35.0 1320.0 1.0 "" "" 12.0 9.5 0.99 1.7 3.0 "" "" 0.001 +818 288.0 1703.6 35.0 1320.0 1.0 "" "" 15.6 9.5 0.99 1.7 2.0 "" "" 0.953 +819 288.0 1362.6 35.0 1320.0 1.0 "" "" 11.1 9.5 0.99 1.7 3.0 "" "" 0.661 +820 288.0 434.7 35.0 1320.0 1.0 "" "" 12.2 9.5 0.99 1.7 1.0 "" "" 0.075 +821 288.0 1323.7 35.0 1320.0 1.0 "" "" 13.9 9.5 0.99 1.7 4.0 "" "" 1.049 +822 288.0 171.3 35.0 1320.0 1.0 "" "" 9.5 9.5 0.99 1.7 3.0 "" "" 0.13 +823 288.0 329.5 35.0 1320.0 1.0 "" "" 9.7 9.5 0.99 1.7 2.0 "" "" 0.369 +824 288.0 563.8 35.0 1320.0 1.0 "" "" 57.3 9.5 0.99 1.7 0.0 "" "" 0.498 +825 288.0 513.6 35.0 1320.0 1.0 "" "" 10.2 9.5 0.99 1.7 2.0 "" "" 0.049 +826 288.0 833.9 35.0 1320.0 1.0 "" "" 16.9 9.5 0.99 1.7 2.0 "" "" 1.049 +827 288.0 734.8 35.0 1320.0 1.0 "" "" 10.5 9.5 0.99 1.7 2.0 "" "" 0.743 +828 288.0 214.4 35.0 1320.0 1.0 "" "" 12.2 9.5 0.99 1.7 5.0 "" "" 0.196 +829 288.0 639.2 35.0 1320.0 1.0 "" "" 12.6 9.5 0.99 1.7 3.0 "" "" 0.32 +830 288.0 393.9 35.0 1320.0 1.0 "" "" 36.9 9.5 0.99 1.7 2.0 "" "" 0.321 +831 288.0 554.7 35.0 1320.0 1.0 "" "" 18.4 9.5 0.99 1.7 3.0 "" "" 0.716 +832 288.0 475.1 35.0 1320.0 1.0 "" "" 18.5 9.5 0.99 1.7 1.0 "" "" 0.149 +833 288.0 441.1 35.0 1320.0 1.0 "" "" 19.2 9.5 0.99 1.7 2.0 "" "" 0.391 +834 288.0 239.7 35.0 1320.0 1.0 "" "" 63.9 9.5 0.99 1.7 2.0 "" "" 0.038 +835 288.0 733.9 35.0 1320.0 1.0 "" "" 18.4 9.5 0.99 1.7 1.0 "" "" 0.785 +836 288.0 706.7 35.0 1320.0 1.0 "" "" 17.0 9.5 0.99 1.7 1.0 "" "" 0.781 +837 288.0 388.2 35.0 1320.0 1.0 "" "" 30.8 9.5 0.99 1.7 3.0 "" "" 0.385 +838 288.0 1225.9 35.0 1320.0 1.0 "" "" 16.8 9.5 0.99 1.7 1.0 "" "" 0.971 +839 288.0 391.6 35.0 1320.0 1.0 "" "" 23.3 9.5 0.99 1.7 3.0 "" "" 0.369 +840 288.0 611.5 35.0 1320.0 1.0 "" "" 16.4 9.5 0.99 1.7 2.0 "" "" 0.205 +841 288.0 1052.3 35.0 1320.0 1.0 "" "" 14.7 9.5 0.99 1.7 3.0 "" "" 0.026 +842 288.0 503.5 35.0 1320.0 1.0 "" "" 13.0 9.5 0.99 1.7 4.0 "" "" 0.376 +843 288.0 809.9 35.0 1320.0 1.0 "" "" 15.8 9.5 0.99 1.7 2.0 "" "" 0.826 +844 288.0 236.2 35.0 1320.0 1.0 "" "" 74.9 9.5 0.99 1.7 2.0 "" "" 0.157 +845 288.0 223.2 35.0 1320.0 1.0 "" "" 24.2 9.5 0.99 1.7 3.0 "" "" 0.079 +846 288.0 343.7 35.0 1320.0 1.0 "" "" 23.3 9.5 0.99 1.7 2.0 "" "" 0.178 +847 288.0 367.6 35.0 1320.0 1.0 "" "" 24.0 9.5 0.99 1.7 0.0 "" "" 0.234 +848 288.0 1253.0 35.0 1320.0 1.0 "" "" 10.5 9.5 0.99 1.7 3.0 "" "" 0.789 +849 288.0 194.5 35.0 1320.0 1.0 "" "" 10.8 9.5 0.99 1.7 2.0 "" "" 0.161 +850 288.0 477.9 35.0 1320.0 1.0 "" "" 14.0 9.5 0.99 1.7 3.0 "" "" 0.519 +851 288.0 362.2 35.0 1320.0 1.0 "" "" 20.1 9.5 0.99 1.7 3.0 "" "" 0.048 +852 288.0 530.0 35.0 1320.0 1.0 "" "" 16.5 9.5 0.99 1.7 0.0 "" "" 0.275 +853 288.0 268.6 35.0 1320.0 1.0 "" "" 13.2 9.5 0.99 1.7 1.0 "" "" 0.001 +854 288.0 899.1 35.0 1320.0 1.0 "" "" 20.0 9.5 0.99 1.7 1.0 "" "" 0.322 +855 288.0 727.1 35.0 1320.0 1.0 "" "" 11.9 9.5 0.99 1.7 3.0 "" "" 0.513 +856 288.0 352.4 35.0 1320.0 1.0 "" "" 36.7 9.5 0.99 1.7 1.0 "" "" 0.014 +857 288.0 395.1 35.0 1320.0 1.0 "" "" 15.9 9.5 0.99 1.7 3.0 "" "" 0.167 +858 288.0 848.2 35.0 1320.0 1.0 "" "" 32.7 9.5 0.99 1.7 3.0 "" "" 0.87 +859 288.0 847.8 35.0 1320.0 1.0 "" "" 15.4 9.5 0.99 1.7 3.0 "" "" 0.909 +860 288.0 526.7 35.0 1320.0 1.0 "" "" 33.0 9.5 0.99 1.7 3.0 "" "" 0.546 +861 288.0 448.5 35.0 1320.0 1.0 "" "" 11.8 9.5 0.99 1.7 3.0 "" "" 0.177 +862 288.0 345.2 35.0 1320.0 1.0 "" "" 12.3 9.5 0.99 1.7 1.0 "" "" 0.249 +863 288.0 799.2 35.0 1320.0 1.0 "" "" 14.5 9.5 0.99 1.7 1.0 "" "" 0.143 +864 288.0 1080.7 35.0 1320.0 1.0 "" "" 12.2 9.5 0.99 1.7 5.0 "" "" 0.122 +865 288.0 892.1 35.0 1320.0 1.0 "" "" 34.4 9.5 0.99 1.7 3.0 "" "" 0.914 +866 288.0 1352.0 35.0 1320.0 1.0 "" "" 15.1 9.5 0.99 1.7 2.0 "" "" 0.982 +867 288.0 674.1 35.0 1320.0 1.0 "" "" 184.9 9.5 0.99 1.7 3.0 "" "" 0.356 +868 288.0 725.3 35.0 1320.0 1.0 "" "" 59.8 9.5 0.99 1.7 1.0 "" "" 0.706 +869 288.0 325.9 35.0 1320.0 1.0 "" "" 12.6 9.5 0.99 1.7 3.0 "" "" 0.3 +870 288.0 1809.5 35.0 1320.0 1.0 "" "" 13.7 9.5 0.99 1.7 2.0 "" "" 0.151 +871 288.0 408.5 35.0 1320.0 1.0 "" "" 69.6 9.5 0.99 1.7 4.0 "" "" 0.342 +872 288.0 533.4 35.0 1320.0 1.0 "" "" 11.3 9.5 0.99 1.7 2.0 "" "" 0.302 +873 288.0 1394.9 35.0 1320.0 1.0 "" "" 10.6 9.5 0.99 1.7 2.0 "" "" 0.888 +874 288.0 466.9 35.0 1320.0 1.0 "" "" 39.7 9.5 0.99 1.7 2.0 "" "" 0.007 +875 288.0 152.1 35.0 1320.0 1.0 "" "" 43.9 9.5 0.99 1.7 1.0 "" "" 0.018 +876 288.0 434.7 35.0 1320.0 1.0 "" "" 15.3 9.5 0.99 1.7 3.0 "" "" 0.232 +877 288.0 676.8 35.0 1320.0 1.0 "" "" 17.4 9.5 0.99 1.7 1.0 "" "" 0.553 +878 288.0 455.1 35.0 1320.0 1.0 "" "" 13.0 9.5 0.99 1.7 0.0 "" "" 0.127 +879 288.0 485.5 35.0 1320.0 1.0 "" "" 30.9 9.5 0.99 1.7 1.0 "" "" 0.142 +880 288.0 1341.2 35.0 1320.0 1.0 "" "" 82.8 9.5 0.99 1.7 0.0 "" "" 0.165 +881 288.0 292.7 35.0 1320.0 1.0 "" "" 124.8 9.5 0.99 1.7 2.0 "" "" 0.248 +882 288.0 256.1 35.0 1320.0 1.0 "" "" 11.5 9.5 0.99 1.7 1.0 "" "" 0.198 +883 288.0 234.8 35.0 1320.0 1.0 "" "" 11.3 9.5 0.99 1.7 3.0 "" "" 0.22 +884 288.0 395.9 35.0 1320.0 1.0 "" "" 9.5 9.5 0.99 1.7 2.0 "" "" 0.173 +885 288.0 384.6 35.0 1320.0 1.0 "" "" 449.6 9.5 0.99 1.7 2.0 "" "" 0.23 +886 288.0 1467.8 35.0 1320.0 1.0 "" "" 14.9 9.5 0.99 1.7 4.0 "" "" 0.81 +887 288.0 167.8 35.0 1320.0 1.0 "" "" 22.8 9.5 0.99 1.7 2.0 "" "" 0.062 +888 288.0 287.6 35.0 1320.0 1.0 "" "" 25.0 9.5 0.99 1.7 3.0 "" "" 0.266 +889 288.0 116.9 35.0 1320.0 1.0 "" "" 89.5 9.5 0.99 1.7 1.0 "" "" 0.033 +890 288.0 211.2 35.0 1320.0 1.0 "" "" 29.9 9.5 0.99 1.7 3.0 "" "" 0.103 +891 288.0 807.2 35.0 1320.0 1.0 "" "" 10.5 9.5 0.99 1.7 2.0 "" "" 0.785 +892 288.0 438.7 35.0 1320.0 1.0 "" "" 14.2 9.5 0.99 1.7 4.0 "" "" 0.478 +893 288.0 175.2 35.0 1320.0 1.0 "" "" 17.4 9.5 0.99 1.7 1.0 "" "" 0.023 +894 288.0 350.0 35.0 1320.0 1.0 "" "" 12.3 9.5 0.99 1.7 3.0 "" "" 0.313 +895 288.0 1865.2 35.0 1320.0 1.0 "" "" 10.4 9.5 0.99 1.7 2.0 "" "" 1.939 +896 288.0 560.0 35.0 1320.0 1.0 "" "" 18.4 9.5 0.99 1.7 2.0 "" "" 0.468 +897 288.0 198.2 35.0 1320.0 1.0 "" "" 233.6 9.5 0.99 1.7 2.0 "" "" 0.039 +898 288.0 585.4 35.0 1320.0 1.0 "" "" 11.1 9.5 0.99 1.7 2.0 "" "" 0.667 +899 288.0 347.7 35.0 1320.0 1.0 "" "" 13.7 9.5 0.99 1.7 2.0 "" "" 0.258 +900 288.0 756.3 35.0 1320.0 1.0 "" "" 10.6 9.5 0.99 1.7 1.0 "" "" 0.889 +901 288.0 210.4 35.0 1320.0 1.0 "" "" 11.4 9.5 0.99 1.7 0.0 "" "" 0.01 +902 288.0 656.0 35.0 1320.0 1.0 "" "" 10.1 9.5 0.99 1.7 4.0 "" "" 0.508 +903 288.0 1539.0 35.0 1320.0 1.0 "" "" 15.9 9.5 0.99 1.7 2.0 "" "" 2.03 +904 288.0 137.6 35.0 1320.0 1.0 "" "" 14.6 9.5 0.99 1.7 2.0 "" "" 0.016 +905 288.0 480.9 35.0 1320.0 1.0 "" "" 45.1 9.5 0.99 1.7 2.0 "" "" 0.081 +906 288.0 506.7 35.0 1320.0 1.0 "" "" 42.2 9.5 0.99 1.7 2.0 "" "" 0.425 +907 288.0 479.0 35.0 1320.0 1.0 "" "" 13.5 9.5 0.99 1.7 4.0 "" "" 0.264 +908 288.0 910.5 35.0 1320.0 1.0 "" "" 12.0 9.5 0.99 1.7 1.0 "" "" 1.062 +909 288.0 764.0 35.0 1320.0 1.0 "" "" 25.4 9.5 0.99 1.7 1.0 "" "" 1.017 +910 288.0 358.5 35.0 1320.0 1.0 "" "" 43.9 9.5 0.99 1.7 4.0 "" "" 0.041 +911 288.0 100.5 35.0 1320.0 1.0 "" "" 92.1 9.5 0.99 1.7 3.0 "" "" 0.007 +912 288.0 403.5 35.0 1320.0 1.0 "" "" 13.4 9.5 0.99 1.7 2.0 "" "" 0.327 +913 288.0 349.2 35.0 1320.0 1.0 "" "" 32.6 9.5 0.99 1.7 4.0 "" "" 0.161 +914 288.0 1399.6 35.0 1320.0 1.0 "" "" 9.7 9.5 0.99 1.7 0.0 "" "" 0.97 +915 288.0 130.8 35.0 1320.0 1.0 "" "" 27.4 9.5 0.99 1.7 2.0 "" "" 0.049 +916 288.0 184.5 35.0 1320.0 1.0 "" "" 10.6 9.5 0.99 1.7 4.0 "" "" 0.092 +917 288.0 553.7 35.0 1320.0 1.0 "" "" 10.1 9.5 0.99 1.7 3.0 "" "" 0.241 +918 288.0 465.2 35.0 1320.0 1.0 "" "" 25.5 9.5 0.99 1.7 2.0 "" "" 0.547 +919 288.0 627.3 35.0 1320.0 1.0 "" "" 31.8 9.5 0.99 1.7 2.0 "" "" 0.41 +920 288.0 629.9 35.0 1320.0 1.0 "" "" 77.8 9.5 0.99 1.7 4.0 "" "" 0.01 +921 288.0 779.1 35.0 1320.0 1.0 "" "" 60.8 9.5 0.99 1.7 2.0 "" "" 0.449 +922 288.0 511.7 35.0 1320.0 1.0 "" "" 12.0 9.5 0.99 1.7 2.0 "" "" 0.154 +923 288.0 224.6 35.0 1320.0 1.0 "" "" 51.8 9.5 0.99 1.7 3.0 "" "" 0.012 +924 288.0 389.8 35.0 1320.0 1.0 "" "" 42.0 9.5 0.99 1.7 3.0 "" "" 0.122 +925 288.0 661.0 35.0 1320.0 1.0 "" "" 15.7 9.5 0.99 1.7 2.0 "" "" 0.818 +926 288.0 993.1 35.0 1320.0 1.0 "" "" 11.5 9.5 0.99 1.7 3.0 "" "" 0.237 +927 288.0 1479.7 35.0 1320.0 1.0 "" "" 9.7 9.5 0.99 1.7 3.0 "" "" 1.498 +928 288.0 476.2 35.0 1320.0 1.0 "" "" 22.0 9.5 0.99 1.7 3.0 "" "" 0.517 +929 288.0 414.2 35.0 1320.0 1.0 "" "" 37.4 9.5 0.99 1.7 3.0 "" "" 0.17 +930 288.0 319.1 35.0 1320.0 1.0 "" "" 15.1 9.5 0.99 1.7 2.0 "" "" 0.201 +931 288.0 1336.7 35.0 1320.0 1.0 "" "" 10.8 9.5 0.99 1.7 3.0 "" "" 1.526 +932 288.0 1027.7 35.0 1320.0 1.0 "" "" 13.1 9.5 0.99 1.7 3.0 "" "" 1.055 +933 288.0 545.3 35.0 1320.0 1.0 "" "" 26.9 9.5 0.99 1.7 3.0 "" "" 0.388 +934 288.0 369.4 35.0 1320.0 1.0 "" "" 10.7 9.5 0.99 1.7 1.0 "" "" 0.01 +935 288.0 168.6 35.0 1320.0 1.0 "" "" 29.4 9.5 0.99 1.7 2.0 "" "" 0.003 +936 288.0 1013.5 35.0 1320.0 1.0 "" "" 13.2 9.5 0.99 1.7 2.0 "" "" 1.012 +937 288.0 254.8 35.0 1320.0 1.0 "" "" 14.9 9.5 0.99 1.7 3.0 "" "" 0.184 +938 288.0 1715.0 35.0 1320.0 1.0 "" "" 12.1 9.5 0.99 1.7 2.0 "" "" 2.012 +939 288.0 2046.3 35.0 1320.0 1.0 "" "" 13.0 9.5 0.99 1.7 0.0 "" "" 1.186 +940 288.0 194.4 35.0 1320.0 1.0 "" "" 35.6 9.5 0.99 1.7 2.0 "" "" 0.031 +941 288.0 235.0 35.0 1320.0 1.0 "" "" 149.4 9.5 0.99 1.7 3.0 "" "" 0.218 +942 288.0 636.0 35.0 1320.0 1.0 "" "" 12.9 9.5 0.99 1.7 3.0 "" "" 0.467 +943 288.0 260.7 35.0 1320.0 1.0 "" "" 15.2 9.5 0.99 1.7 3.0 "" "" 0.118 +944 288.0 923.6 35.0 1320.0 1.0 "" "" 18.1 9.5 0.99 1.7 3.0 "" "" 0.803 +945 288.0 913.5 35.0 1320.0 1.0 "" "" 24.7 9.5 0.99 1.7 1.0 "" "" 1.208 +946 288.0 200.0 35.0 1320.0 1.0 "" "" 11.0 9.5 0.99 1.7 3.0 "" "" 0.155 +947 288.0 363.2 35.0 1320.0 1.0 "" "" 12.0 9.5 0.99 1.7 3.0 "" "" 0.386 +948 288.0 324.1 35.0 1320.0 1.0 "" "" 22.7 9.5 0.99 1.7 1.0 "" "" 0.178 +949 288.0 497.9 35.0 1320.0 1.0 "" "" 24.1 9.5 0.99 1.7 3.0 "" "" 0.549 +950 288.0 464.8 35.0 1320.0 1.0 "" "" 10.6 9.5 0.99 1.7 4.0 "" "" 0.503 +951 288.0 897.2 35.0 1320.0 1.0 "" "" 19.2 9.5 0.99 1.7 3.0 "" "" 0.893 +952 288.0 243.3 35.0 1320.0 1.0 "" "" 12.5 9.5 0.99 1.7 2.0 "" "" 0.111 +953 288.0 340.6 35.0 1320.0 1.0 "" "" 9.9 9.5 0.99 1.7 2.0 "" "" 0.173 +954 288.0 239.4 35.0 1320.0 1.0 "" "" 11.0 9.5 0.99 1.7 3.0 "" "" 0.24 +955 288.0 672.0 35.0 1320.0 1.0 "" "" 12.1 9.5 0.99 1.7 3.0 "" "" 0.618 +956 288.0 918.3 35.0 1320.0 1.0 "" "" 19.9 9.5 0.99 1.7 3.0 "" "" 0.45 +957 288.0 351.4 35.0 1320.0 1.0 "" "" 27.7 9.5 0.99 1.7 3.0 "" "" 0.394 +958 288.0 865.6 35.0 1320.0 1.0 "" "" 10.3 9.5 0.99 1.7 0.0 "" "" 1.064 +959 288.0 1634.9 35.0 1320.0 1.0 "" "" 15.9 9.5 0.99 1.7 3.0 "" "" 1.946 +960 288.0 375.6 35.0 1320.0 1.0 "" "" 9.6 9.5 0.99 1.7 1.0 "" "" 0.255 +961 288.0 1282.2 35.0 1320.0 1.0 "" "" 12.7 9.5 0.99 1.7 3.0 "" "" 1.503 +962 288.0 810.4 35.0 1320.0 1.0 "" "" 22.6 9.5 0.99 1.7 3.0 "" "" 0.109 +963 288.0 1615.8 35.0 1320.0 1.0 "" "" 14.0 9.5 0.99 1.7 4.0 "" "" 0.363 +964 288.0 410.2 35.0 1320.0 1.0 "" "" 14.9 9.5 0.99 1.7 2.0 "" "" 0.229 +965 288.0 322.5 35.0 1320.0 1.0 "" "" 37.2 9.5 0.99 1.7 4.0 "" "" 0.171 +966 288.0 807.4 35.0 1320.0 1.0 "" "" 20.6 9.5 0.99 1.7 3.0 "" "" 0.926 +967 288.0 809.0 35.0 1320.0 1.0 "" "" 15.2 9.5 0.99 1.7 3.0 "" "" 0.973 +968 288.0 200.8 35.0 1320.0 1.0 "" "" 40.9 9.5 0.99 1.7 1.0 "" "" 0.017 +969 288.0 624.5 35.0 1320.0 1.0 "" "" 29.0 9.5 0.99 1.7 3.0 "" "" 0.608 +970 288.0 765.5 35.0 1320.0 1.0 "" "" 27.9 9.5 0.99 1.7 3.0 "" "" 0.903 +971 288.0 723.4 35.0 1320.0 1.0 "" "" 18.6 9.5 0.99 1.7 2.0 "" "" 0.748 +972 288.0 1077.4 35.0 1320.0 1.0 "" "" 17.1 9.5 0.99 1.7 2.0 "" "" 0.677 +973 288.0 817.6 35.0 1320.0 1.0 "" "" 11.1 9.5 0.99 1.7 4.0 "" "" 0.672 +974 288.0 1497.4 35.0 1320.0 1.0 "" "" 10.0 9.5 0.99 1.7 4.0 "" "" 1.515 +975 288.0 569.8 35.0 1320.0 1.0 "" "" 57.6 9.5 0.99 1.7 3.0 "" "" 0.124 +976 288.0 510.3 35.0 1320.0 1.0 "" "" 33.4 9.5 0.99 1.7 2.0 "" "" 0.48 +977 288.0 452.5 35.0 1320.0 1.0 "" "" 10.3 9.5 0.99 1.7 4.0 "" "" 0.265 +978 288.0 975.6 35.0 1320.0 1.0 "" "" 11.2 9.5 0.99 1.7 3.0 "" "" 1.173 +979 288.0 496.8 35.0 1320.0 1.0 "" "" 55.7 9.5 0.99 1.7 1.0 "" "" 0.375 +980 288.0 199.7 35.0 1320.0 1.0 "" "" 9.9 9.5 0.99 1.7 3.0 "" "" 0.038 +981 288.0 806.6 35.0 1320.0 1.0 "" "" 14.6 9.5 0.99 1.7 2.0 "" "" 0.794 +982 288.0 642.3 35.0 1320.0 1.0 "" "" 13.4 9.5 0.99 1.7 3.0 "" "" 0.439 +983 288.0 959.5 35.0 1320.0 1.0 "" "" 16.6 9.5 0.99 1.7 0.0 "" "" 0.787 +984 288.0 396.7 35.0 1320.0 1.0 "" "" 21.8 9.5 0.99 1.7 3.0 "" "" 0.33 +985 288.0 142.1 35.0 1320.0 1.0 "" "" 21.8 9.5 0.99 1.7 3.0 "" "" 0.043 +986 288.0 376.1 35.0 1320.0 1.0 "" "" 19.0 9.5 0.99 1.7 1.0 "" "" 0.219 +987 288.0 1077.2 35.0 1320.0 1.0 "" "" 35.2 9.5 0.99 1.7 1.0 "" "" 1.389 +988 288.0 632.7 35.0 1320.0 1.0 "" "" 11.9 9.5 0.99 1.7 0.0 "" "" 0.475 +989 288.0 330.1 35.0 1320.0 1.0 "" "" 11.7 9.5 0.99 1.7 3.0 "" "" 0.163 +990 288.0 247.1 35.0 1320.0 1.0 "" "" 18.0 9.5 0.99 1.7 3.0 "" "" 0.152 +991 288.0 140.6 35.0 1320.0 1.0 "" "" 20.3 9.5 0.99 1.7 3.0 "" "" 0.019 +992 288.0 578.6 35.0 1320.0 1.0 "" "" 53.0 9.5 0.99 1.7 1.0 "" "" 0.301 +993 288.0 230.2 35.0 1320.0 1.0 "" "" 10.1 9.5 0.99 1.7 2.0 "" "" 0.125 +994 288.0 420.1 35.0 1320.0 1.0 "" "" 18.6 9.5 0.99 1.7 3.0 "" "" 0.135 +995 288.0 579.4 35.0 1320.0 1.0 "" "" 14.9 9.5 0.99 1.7 1.0 "" "" 0.36 +996 288.0 1031.4 35.0 1320.0 1.0 "" "" 14.7 9.5 0.99 1.7 2.0 "" "" 1.19 +997 288.0 391.9 35.0 1320.0 1.0 "" "" 20.3 9.5 0.99 1.7 5.0 "" "" 0.147 +998 288.0 341.4 35.0 1320.0 1.0 "" "" 79.0 9.5 0.99 1.7 1.0 "" "" 0.267 +999 288.0 442.7 35.0 1320.0 1.0 "" "" 14.0 9.5 0.99 1.7 3.0 "" "" 0.433 diff --git a/zdm/grid.py b/zdm/grid.py index de90fa06..b3a4cb9c 100644 --- a/zdm/grid.py +++ b/zdm/grid.py @@ -66,7 +66,7 @@ def __init__(self, survey, state, zDMgrid, zvals, dmvals, smear_mask, wdist): else: efficiencies = survey.mean_efficiencies weights = None - self.calc_thresholds(survey.meta["THRESH"], efficiencies, weights=weights) + self.calc_thresholds(survey.meta["THRESH"], efficiencies, weights=weights) efficiencies=survey.mean_efficiencies weights=None # Warning -- THRESH could be different for each FRB, but we don't treat it that way diff --git a/zdm/real_loading.py b/zdm/real_loading.py index 47872b80..75a5815e 100644 --- a/zdm/real_loading.py +++ b/zdm/real_loading.py @@ -119,9 +119,9 @@ def surveys_and_grids(init_state=None, alpha_method=1, add_20220610A=False): ############## Initialise surveys ############## survey_names = ['CRAFT/FE', - 'CRAFT_ICS_1632', - 'CRAFT_ICS_892', - 'CRAFT_ICS', + 'private_CRAFT_ICS_1632', + 'private_CRAFT_ICS_892', + 'private_CRAFT_ICS_1272', 'PKS/Mb'] if add_20220610A: survey_names[3] = 'CRAFT_ICS_w_220610' @@ -132,7 +132,7 @@ def surveys_and_grids(init_state=None, alpha_method=1, add_20220610A=False): print(f"Initializing {survey_name}") surveys.append(survey.load_survey(survey_name, state, dmvals, - Nbeams=nbeam)) + nbins=nbeam)) print("Initialised surveys") # generates zdm grid diff --git a/zdm/scripts/plot_limits_from_cube.py b/zdm/scripts/plot_limits_from_cube.py index 885edc11..f90af8ad 100644 --- a/zdm/scripts/plot_limits_from_cube.py +++ b/zdm/scripts/plot_limits_from_cube.py @@ -31,6 +31,7 @@ def main(verbose=False): Reiss_sigma = 1.42 ##### loads cube data ##### + # cube = "../../papers/F/Analysis/Real/Cubes/craco_real_cube.npz" cube = "../../papers/F/Analysis/CRACO/Cubes/craco_full_cube.npz" data = np.load(cube) if verbose: diff --git a/zdm/survey.py b/zdm/survey.py index 4c64905d..94236442 100644 --- a/zdm/survey.py +++ b/zdm/survey.py @@ -523,16 +523,16 @@ def process_survey_file(self,filename:str, self.NFRB=len(self.frbs) else: self.NFRB=min(len(self.frbs), NFRB) - if self.NFRB < NFRB+iFRB: + if self.NFRB > NFRB+iFRB: raise ValueError("Cannot return sufficient FRBs, did you mean NFRB=None?") # Not sure the following linematters given the Error above - themax = min(NFRB+iFRB,self.NFRB) + themax = max(NFRB+iFRB,self.NFRB) self.frbs=self.frbs[iFRB:themax] # Vet vet_frb_table(self.frbs, mandatory=True) # Pandas resolves None to Nan - if np.isfinite(self.frbs["Z"][0]): + if np.isfinite(self.frbs["Z"].values[0]): self.Zs=self.frbs["Z"].values # checks for any redhsifts identically equal to zero @@ -980,13 +980,13 @@ def load_survey(survey_name:str, state:parameters.State, dfile = 'CRAFT_class_I_and_II' nbins = 5 elif survey_name == 'CRAFT/ICS': - dfile = 'CRAFT_ICS' + dfile = 'private_CRAFT_ICS_1272' nbins = 5 elif survey_name == 'CRAFT/ICS892': - dfile = 'CRAFT_ICS_892' + dfile = 'private_CRAFT_ICS_892' nbins = 5 elif survey_name == 'CRAFT/ICS1632': - dfile = 'CRAFT_ICS_1632' + dfile = 'private_CRAFT_ICS_1632' nbins = 5 elif survey_name == 'PKS/Mb': dfile = 'parkes_mb_class_I_and_II' @@ -1044,9 +1044,16 @@ def refactor_old_survey_file(survey_name:str, outfile:str, srvy_data = survey_data.SurveyData() # Load up original + + # temporary workaround for private survey files... sorry X! + + nbins = None + if 'private' in survey_name: + nbins = 5 # only works for the CRAFT private samples + isurvey = load_survey(survey_name, state, np.linspace(0., 2000., 1000), - original=True) + original=True, nbins=nbins) # FRBs frbs = pandas.DataFrame(isurvey.frbs) From cdd7a48721b675eeb9095e265702828ddb5549df Mon Sep 17 00:00:00 2001 From: Jay Baptista Date: Sat, 28 Jan 2023 19:22:53 -0500 Subject: [PATCH 088/104] increase H0 range for real cube json --- papers/F/Analysis/Real/Cubes/craco_real_cube.json | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/papers/F/Analysis/Real/Cubes/craco_real_cube.json b/papers/F/Analysis/Real/Cubes/craco_real_cube.json index b1a2ea18..03e29078 100644 --- a/papers/F/Analysis/Real/Cubes/craco_real_cube.json +++ b/papers/F/Analysis/Real/Cubes/craco_real_cube.json @@ -22,8 +22,8 @@ "H0": { "DC": "cosmo", "min": 60.0, - "max": 80.0, - "n": 21 + "max": 100.0, + "n": 41 }, "lmean": { "DC": "host", From a315e6e99feac47924479ae8b9a2bfa67ff10c46 Mon Sep 17 00:00:00 2001 From: Jay Baptista Date: Sat, 28 Jan 2023 22:27:05 -0500 Subject: [PATCH 089/104] require new version of astropy --- setup.cfg | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.cfg b/setup.cfg index 5f524440..20023e1f 100644 --- a/setup.cfg +++ b/setup.cfg @@ -32,7 +32,7 @@ setup_requires = setuptools_scm include_package_data = True install_requires = numpy>=1.18 - astropy>=4.0 + astropy>=5.0 scipy>=1.4 matplotlib>=3.3 PyYAML>=5.1 From 304008668745b1638ade582e0cc38bf25471d7bb Mon Sep 17 00:00:00 2001 From: Jay Baptista Date: Sun, 29 Jan 2023 14:54:02 -0500 Subject: [PATCH 090/104] using an even newer version of astropy --- setup.cfg | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.cfg b/setup.cfg index 20023e1f..747e60da 100644 --- a/setup.cfg +++ b/setup.cfg @@ -32,7 +32,7 @@ setup_requires = setuptools_scm include_package_data = True install_requires = numpy>=1.18 - astropy>=5.0 + astropy>=5.2.1 scipy>=1.4 matplotlib>=3.3 PyYAML>=5.1 From a94ccc47ebdde3b13186a349686cb8714aec8699 Mon Sep 17 00:00:00 2001 From: Jay Baptista Date: Wed, 1 Feb 2023 16:36:48 -0500 Subject: [PATCH 091/104] increase z-dm grid resolution in real cube --- zdm/real_loading.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/zdm/real_loading.py b/zdm/real_loading.py index 75a5815e..acedd632 100644 --- a/zdm/real_loading.py +++ b/zdm/real_loading.py @@ -114,7 +114,7 @@ def surveys_and_grids(init_state=None, alpha_method=1, add_20220610A=False): # get the grid of p(DM|z) zDMgrid, zvals,dmvals = misc_functions.get_zdm_grid( - state, new=True, plot=False, method='analytic', nz=500, + state, new=True, plot=False, method='analytic', nz=2000, ndm=5600, datdir=resource_filename('zdm', 'GridData')) ############## Initialise surveys ############## From 3f800117b2a7bd40baca16ee3fb7f9b4ea262bc7 Mon Sep 17 00:00:00 2001 From: Jay Baptista Date: Sun, 19 Feb 2023 19:16:31 -0500 Subject: [PATCH 092/104] craco run script for nautilus jupyterhub --- .../Real/Cloud/run_real_craco_block.py | 26 +++++++++++++++++++ 1 file changed, 26 insertions(+) create mode 100644 papers/F/Analysis/Real/Cloud/run_real_craco_block.py diff --git a/papers/F/Analysis/Real/Cloud/run_real_craco_block.py b/papers/F/Analysis/Real/Cloud/run_real_craco_block.py new file mode 100644 index 00000000..f3d3285a --- /dev/null +++ b/papers/F/Analysis/Real/Cloud/run_real_craco_block.py @@ -0,0 +1,26 @@ +# Running this command: python ../py/build_real_cube.py -n 1 -m 3000 -o Output/craco_real1.csv --clobber -p ../Cubes/craco_real_cube.json + +import numpy as np +import subprocess + +start = 1 +end = 41 +nums = np.arange(start, end + 1, dtype="int") + +commands = [] + +for number in nums: + line = f"python ../py/build_real_cube.py -n {number} -m 3000 -o Output/craco_real{number}.csv --clobber -p ../Cubes/craco_real_cube.json" + commands.append(line) + +processes = [] + +for command in commands: + print(f"Running this command: {' '.join(command)}") + pw = subprocess.Popen(command) + processes.append(pw) + +for pw in processes: + exit_code = pw.wait() + print(exit_code) + From 7a4fca15fdb0c48f6a3332392c3229d2215975c2 Mon Sep 17 00:00:00 2001 From: Jay Baptista Date: Sun, 19 Feb 2023 19:28:58 -0500 Subject: [PATCH 093/104] fix lines for block script --- .../Real/Cloud/run_real_craco_block.py | 19 +++++++++++++++++-- 1 file changed, 17 insertions(+), 2 deletions(-) diff --git a/papers/F/Analysis/Real/Cloud/run_real_craco_block.py b/papers/F/Analysis/Real/Cloud/run_real_craco_block.py index f3d3285a..3d8230f4 100644 --- a/papers/F/Analysis/Real/Cloud/run_real_craco_block.py +++ b/papers/F/Analysis/Real/Cloud/run_real_craco_block.py @@ -10,8 +10,23 @@ commands = [] for number in nums: - line = f"python ../py/build_real_cube.py -n {number} -m 3000 -o Output/craco_real{number}.csv --clobber -p ../Cubes/craco_real_cube.json" - commands.append(line) + # line = f"python ../py/build_real_cube.py -n {number} -m 3000 -o Output/craco_real{number}.csv --clobber -p ../Cubes/craco_real_cube.json" + # commands.append(line) + + # Command + line = [ + "python", + "../py/build_real_cube.py", + "-n", + f"{number}", + "-m", + "3000", + "-o", + f"Output/craco_real{number}.csv", + "--clobber", + "-p", + f"../Cubes/craco_real_cube.json", + ] processes = [] From b027bd43e6a5cdb217473c9380f7416d6d0dffa3 Mon Sep 17 00:00:00 2001 From: Jay Baptista Date: Mon, 20 Feb 2023 14:02:50 -0500 Subject: [PATCH 094/104] add argparse to block run --- .../Real/Cloud/run_real_craco_block.py | 99 ++++++++++++------- 1 file changed, 64 insertions(+), 35 deletions(-) diff --git a/papers/F/Analysis/Real/Cloud/run_real_craco_block.py b/papers/F/Analysis/Real/Cloud/run_real_craco_block.py index 3d8230f4..364baccb 100644 --- a/papers/F/Analysis/Real/Cloud/run_real_craco_block.py +++ b/papers/F/Analysis/Real/Cloud/run_real_craco_block.py @@ -1,41 +1,70 @@ # Running this command: python ../py/build_real_cube.py -n 1 -m 3000 -o Output/craco_real1.csv --clobber -p ../Cubes/craco_real_cube.json +import argparse import numpy as np import subprocess -start = 1 -end = 41 -nums = np.arange(start, end + 1, dtype="int") - -commands = [] - -for number in nums: - # line = f"python ../py/build_real_cube.py -n {number} -m 3000 -o Output/craco_real{number}.csv --clobber -p ../Cubes/craco_real_cube.json" - # commands.append(line) - - # Command - line = [ - "python", - "../py/build_real_cube.py", - "-n", - f"{number}", - "-m", - "3000", - "-o", - f"Output/craco_real{number}.csv", - "--clobber", - "-p", - f"../Cubes/craco_real_cube.json", - ] - -processes = [] - -for command in commands: - print(f"Running this command: {' '.join(command)}") - pw = subprocess.Popen(command) - processes.append(pw) - -for pw in processes: - exit_code = pw.wait() - print(exit_code) +def main(pargs): + start = pargs.start + end = pargs.end + nums = np.arange(start, end + 1, dtype="int") + commands = [] + + for number in nums: + + line = [ + "python", + "../py/build_real_cube.py", + "-n", + f"{number}", + "-m", + "3000", + "-o", + f"Output/craco_real{number}.csv", + "--clobber", + "-p", + f"../Cubes/craco_real_cube.json", + ] + + commands.append(line) + + processes = [] + + for command in commands: + print(f"Running this command: {' '.join(command)}") + pw = subprocess.Popen(command) + processes.append(pw) + + for pw in processes: + exit_code = pw.wait() + print(exit_code) + + print("All done!") + +def parse_option(): + # test for command-line arguments here + parser = argparse.ArgumentParser() + parser.add_argument( + "-s", + "--start", + type=int, + required=True, + help="csv to start on", + ) + parser.add_argument( + "-e", + "--end", + type=int, + required=False, + help="csv to end on (inclusive)", + ) + + args = parser.parse_args() + + return args + +if __name__ == "__main__": + # get the argument of training. + pargs = parse_option() + main(pargs) \ No newline at end of file From 633082390b23bf2d4c207735d7bda5b609fdb29e Mon Sep 17 00:00:00 2001 From: Jay Baptista Date: Mon, 20 Feb 2023 14:19:55 -0500 Subject: [PATCH 095/104] update --- .../Real/Cloud/run_real_craco_block.py | 74 +++++++++---------- 1 file changed, 35 insertions(+), 39 deletions(-) diff --git a/papers/F/Analysis/Real/Cloud/run_real_craco_block.py b/papers/F/Analysis/Real/Cloud/run_real_craco_block.py index 364baccb..4705dd34 100644 --- a/papers/F/Analysis/Real/Cloud/run_real_craco_block.py +++ b/papers/F/Analysis/Real/Cloud/run_real_craco_block.py @@ -4,67 +4,63 @@ import numpy as np import subprocess + def main(pargs): - start = pargs.start - end = pargs.end - nums = np.arange(start, end + 1, dtype="int") - commands = [] + print(f"Running batch from CSVs {pargs.start} to {pargs.end}") + start = pargs.start + end = pargs.end + nums = np.arange(start, end + 1, dtype="int") + + commands = [] - for number in nums: + for number in nums: - line = [ - "python", - "../py/build_real_cube.py", - "-n", - f"{number}", - "-m", - "3000", - "-o", - f"Output/craco_real{number}.csv", - "--clobber", - "-p", - f"../Cubes/craco_real_cube.json", - ] + line = [ + "python", + "../py/build_real_cube.py", + "-n", + f"{number}", + "-m", + "3000", + "-o", + f"Output/craco_real{number}.csv", + "--clobber", + "-p", + f"../Cubes/craco_real_cube.json", + ] + commands.append(line) - commands.append(line) + processes = [] - processes = [] + for command in commands: + print(f"Running this command: {' '.join(command)}") + pw = subprocess.Popen(command) + processes.append(pw) - for command in commands: - print(f"Running this command: {' '.join(command)}") - pw = subprocess.Popen(command) - processes.append(pw) + for pw in processes: + exit_code = pw.wait() + print(exit_code) - for pw in processes: - exit_code = pw.wait() - print(exit_code) + print("All done!") - print("All done!") def parse_option(): # test for command-line arguments here parser = argparse.ArgumentParser() parser.add_argument( - "-s", - "--start", - type=int, - required=True, - help="csv to start on", + "-s", "--start", type=int, required=True, help="csv to start on", ) parser.add_argument( - "-e", - "--end", - type=int, - required=False, - help="csv to end on (inclusive)", + "-e", "--end", type=int, required=False, help="csv to end on (inclusive)", ) args = parser.parse_args() return args + if __name__ == "__main__": # get the argument of training. pargs = parse_option() - main(pargs) \ No newline at end of file + main(pargs) From d4bf64ea5fad82580db7b84975e3bd74f2f45f46 Mon Sep 17 00:00:00 2001 From: Jay Baptista Date: Wed, 26 Apr 2023 20:30:37 -0400 Subject: [PATCH 096/104] new nautilus run files --- .vscode/settings.json | 6 + papers/F/Analysis/CRACO/testF.py | 6 +- .../Analysis/Real/Cloud/Output/explore.ipynb | 465 ++++++ .../Real/Cloud/Output_low_res/explore.ipynb | 105 ++ .../Real/Cloud/nautilus_real_cube.yaml | 17 +- .../Real/Cloud/nautilus_real_mini.yaml | 3 + .../Cloud/yamls/nautilus_real_cube_b1.yaml | 84 + .../Cloud/yamls/nautilus_real_cube_b2.yaml | 84 + .../Cloud/yamls/nautilus_real_cube_b3.yaml | 84 + .../Cloud/yamls/nautilus_real_cube_b4.yaml | 84 + .../Cloud/yamls/nautilus_real_cube_b5.yaml | 84 + .../Cloud/yamls/nautilus_real_cube_b6.yaml | 84 + .../Cloud/yamls/nautilus_real_cube_b7.yaml | 84 + .../Cloud/yamls/nautilus_real_cube_b8.yaml | 84 + papers/F/Analysis/Real/cube_diag.ipynb | 1377 +++++++++++++++++ .../Analysis/Real/logF_host_comparison.ipynb | 2 +- papers/F/Analysis/Real/make_fig10.py | 59 + papers/F/Analysis/Real/make_fig9.py | 156 ++ papers/F/Analysis/Real/test.py | 152 ++ papers/F/Analysis/Real/testF.py | 2 +- papers/F/Analysis/Real/testing_bayesian.ipynb | 186 +++ papers/F/Analysis/Real/tmp.ipynb | 150 -- papers/F/Analysis/py/get_PDFs.py | 160 ++ papers/F/Analysis/py/plot_limits_from_cube.py | 193 +++ zdm/analyze_cube.py | 58 +- zdm/scripts/plot_limits_from_cube.py | 9 +- 26 files changed, 3609 insertions(+), 169 deletions(-) create mode 100644 .vscode/settings.json create mode 100644 papers/F/Analysis/Real/Cloud/Output/explore.ipynb create mode 100644 papers/F/Analysis/Real/Cloud/Output_low_res/explore.ipynb create mode 100644 papers/F/Analysis/Real/Cloud/yamls/nautilus_real_cube_b1.yaml create mode 100644 papers/F/Analysis/Real/Cloud/yamls/nautilus_real_cube_b2.yaml create mode 100644 papers/F/Analysis/Real/Cloud/yamls/nautilus_real_cube_b3.yaml create mode 100644 papers/F/Analysis/Real/Cloud/yamls/nautilus_real_cube_b4.yaml create mode 100644 papers/F/Analysis/Real/Cloud/yamls/nautilus_real_cube_b5.yaml create mode 100644 papers/F/Analysis/Real/Cloud/yamls/nautilus_real_cube_b6.yaml create mode 100644 papers/F/Analysis/Real/Cloud/yamls/nautilus_real_cube_b7.yaml create mode 100644 papers/F/Analysis/Real/Cloud/yamls/nautilus_real_cube_b8.yaml create mode 100644 papers/F/Analysis/Real/cube_diag.ipynb create mode 100644 papers/F/Analysis/Real/make_fig10.py create mode 100644 papers/F/Analysis/Real/make_fig9.py create mode 100644 papers/F/Analysis/Real/test.py create mode 100644 papers/F/Analysis/Real/testing_bayesian.ipynb delete mode 100644 papers/F/Analysis/Real/tmp.ipynb create mode 100644 papers/F/Analysis/py/get_PDFs.py create mode 100644 papers/F/Analysis/py/plot_limits_from_cube.py diff --git a/.vscode/settings.json b/.vscode/settings.json new file mode 100644 index 00000000..6ba1afd2 --- /dev/null +++ b/.vscode/settings.json @@ -0,0 +1,6 @@ +{ + "[python]": { + "editor.defaultFormatter": "ms-python.black-formatter" + }, + "python.formatting.provider": "none" +} \ No newline at end of file diff --git a/papers/F/Analysis/CRACO/testF.py b/papers/F/Analysis/CRACO/testF.py index 33b5b272..7f087c97 100644 --- a/papers/F/Analysis/CRACO/testF.py +++ b/papers/F/Analysis/CRACO/testF.py @@ -9,11 +9,11 @@ def main(verbose=False): # output directory - opdir="figs/" + opdir="2d_figs/" if not os.path.exists(opdir): os.mkdir(opdir) - CubeFile='Cubes/craco_mini_cube.npz' + CubeFile='Cubes/craco_full_cube.npz' if os.path.exists(CubeFile): data=np.load(CubeFile) else: @@ -105,7 +105,7 @@ def get_param_values(data,params): # log to lin space array[np.isnan(array)] = -1e99 - array -= np.max(array) + array -= np.nanmax(array) array = 10**array array /= np.sum(array) diff --git a/papers/F/Analysis/Real/Cloud/Output/explore.ipynb b/papers/F/Analysis/Real/Cloud/Output/explore.ipynb new file mode 100644 index 00000000..b52518b2 --- /dev/null +++ b/papers/F/Analysis/Real/Cloud/Output/explore.ipynb @@ -0,0 +1,465 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "from astropy.table import Table\n", + "import numpy as np\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "vals = []\n", + "for k in np.arange(1, 42):\n", + " tab = pd.read_csv(f\"craco_real{k}.csv\")\n", + " vals.append(sum(np.isnan(tab.lls)))" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ True, True, True, True, True, False, False, False, False,\n", + " False, False, False, False, False, False, False, False, False,\n", + " False, False, False, False, False, False, False, False, False,\n", + " False, False, False, False, False, False, False, False, False,\n", + " False, False, False, False, False])" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.array(vals) > 0" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "tab = pd.read_csv(f\"craco_real1.csv\")" + ] + }, + { + "cell_type": "code", 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"python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "9731a3b7ebb41545a410367c249afbc4842fd5740da82f1bd29e6ac1d7253e6e" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/papers/F/Analysis/Real/Cloud/Output_low_res/explore.ipynb b/papers/F/Analysis/Real/Cloud/Output_low_res/explore.ipynb new file mode 100644 index 00000000..5f2da120 --- /dev/null +++ b/papers/F/Analysis/Real/Cloud/Output_low_res/explore.ipynb @@ -0,0 +1,105 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from astropy.table import Table\n", + "import numpy as np\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "vals = []\n", + "for k in np.arange(1, 42):\n", + " tab = pd.read_csv(f\"craco_real{k}.csv\")\n", + " vals.append(sum(np.isnan(tab.lls)))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ True, True, True, True, False, False, False, False, False,\n", + " False, False, False, False, False, False, False, False, False,\n", + " False, False, False, False, False, False, False, False, False,\n", + " False, False, False, False, False, False, False, False, False,\n", + " False, False, False, False, False])" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.array(vals) > 0" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "600" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.sum(vals)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "9731a3b7ebb41545a410367c249afbc4842fd5740da82f1bd29e6ac1d7253e6e" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/papers/F/Analysis/Real/Cloud/nautilus_real_cube.yaml b/papers/F/Analysis/Real/Cloud/nautilus_real_cube.yaml index 35bb1eb3..0b0aa215 100644 --- a/papers/F/Analysis/Real/Cloud/nautilus_real_cube.yaml +++ b/papers/F/Analysis/Real/Cloud/nautilus_real_cube.yaml @@ -27,13 +27,13 @@ spec: imagePullPolicy: Always resources: requests: - cpu: "21" - memory: "25Gi" # - ephemeral-storage: 50Gi # + cpu: "20" + memory: "64Gi" # + ephemeral-storage: 128Gi # limits: - cpu: "23" - memory: "50Gi" - ephemeral-storage: 100Gi + cpu: "30" + memory: "80Gi" + ephemeral-storage: 200Gi #nvidia.com/gpu: "1" # See docs to exlude certain types command: ["/bin/bash", "-c"] args: @@ -47,9 +47,12 @@ spec: git fetch; git checkout varying_F; python setup.py develop; + cd zdm/data/Surveys; + aws --endpoint http://rook-ceph-rgw-nautiluss3.rook s3 cp s3://zdm/Surveys . --recursive --force; + cd ../../..; cd papers/F/Analysis/Real/Cloud; mkdir Output; - python run_craco_real.py -n 21 -t 21 -b 1; + python run_craco_real.py -n 41 -t 41 -b 1; aws --endpoint http://rook-ceph-rgw-nautiluss3.rook s3 cp Output s3://zdm/Cubes/F/real/ --recursive --force; env: - name: "ENDPOINT_URL" diff --git a/papers/F/Analysis/Real/Cloud/nautilus_real_mini.yaml b/papers/F/Analysis/Real/Cloud/nautilus_real_mini.yaml index 4aeadedd..dfc52acf 100644 --- a/papers/F/Analysis/Real/Cloud/nautilus_real_mini.yaml +++ b/papers/F/Analysis/Real/Cloud/nautilus_real_mini.yaml @@ -47,6 +47,9 @@ spec: git fetch; git checkout varying_F; python setup.py develop; + cd zdm/data/Surveys; + aws --endpoint http://rook-ceph-rgw-nautiluss3.rook s3 cp s3://zdm/Surveys . --recursive --force; + cd ../../..; cd papers/F/Analysis/Real/Cloud; mkdir Output; python run_real_mini.py -n 5 -t 5 -b 1; diff --git a/papers/F/Analysis/Real/Cloud/yamls/nautilus_real_cube_b1.yaml b/papers/F/Analysis/Real/Cloud/yamls/nautilus_real_cube_b1.yaml new file mode 100644 index 00000000..5bad8793 --- /dev/null +++ b/papers/F/Analysis/Real/Cloud/yamls/nautilus_real_cube_b1.yaml @@ -0,0 +1,84 @@ +# 21 processors on full for Varying F +# kubectl exec -it test-pod -- /bin/bash +apiVersion: batch/v1 +kind: Job +metadata: + name: jb-zdm-craco-real-logf-b1 +spec: + backoffLimit: 0 + template: + spec: + affinity: + nodeAffinity: + requiredDuringSchedulingIgnoredDuringExecution: + nodeSelectorTerms: + - matchExpressions: + - key: kubernetes.io/hostname + operator: NotIn + values: + - k8s-chase-ci-01.noc.ucsb.edu + - key: nvidia.com/gpu.product + operator: In + values: + - NVIDIA-GeForce-GTX-1080-Ti + containers: + - name: container + image: localhost:30081/profxj/zdm_docker:latest # UPDATE + imagePullPolicy: Always + resources: + requests: + cpu: "6" + memory: "64Gi" # + ephemeral-storage: 64Gi # + limits: + cpu: "8" + memory: "80Gi" + ephemeral-storage: 80Gi + #nvidia.com/gpu: "1" # See docs to exlude certain types + command: ["/bin/bash", "-c"] + args: + - cd FRB; + git fetch; + git pull; + python setup.py develop; + cd ../ne2001; + python setup.py develop; + cd ../zdm; + git fetch; + git checkout varying_F; + python setup.py develop; + cd zdm/data/Surveys; + aws --endpoint http://rook-ceph-rgw-nautiluss3.rook s3 cp s3://zdm/Surveys . --recursive --force; + cd ../../..; + cd papers/F/Analysis/Real/Cloud; + mkdir Output; + python run_real_craco_block.py -s 1 -e 5; + aws --endpoint http://rook-ceph-rgw-nautiluss3.rook s3 cp Output s3://zdm/Cubes/F/real/ --recursive --force; + env: + - name: "ENDPOINT_URL" + value: "http://rook-ceph-rgw-nautiluss3.rook" + - name: "S3_ENDPOINT" + value: "rook-ceph-rgw-nautiluss3.rook" + volumeMounts: + - name: prp-s3-credentials + mountPath: "/root/.aws/credentials" + subPath: "credentials" + - name: ephemeral + mountPath: "/tmp" + - name: "dshm" + mountPath: "/dev/shm" + nodeSelector: + nautilus.io/disktype: nvme + restartPolicy: Never + volumes: + # Secrets file for nautilus s3 credentials .aws/credentials and .s3cfg + - name: prp-s3-credentials + secret: + secretName: prp-s3-credentials + # Shared memory (necessary for Python's multiprocessing.shared_memory module to work) + - name: dshm + emptyDir: + medium: Memory + # Ephemeral storage + - name: ephemeral + emptyDir: {} diff --git a/papers/F/Analysis/Real/Cloud/yamls/nautilus_real_cube_b2.yaml b/papers/F/Analysis/Real/Cloud/yamls/nautilus_real_cube_b2.yaml new file mode 100644 index 00000000..633b07c3 --- /dev/null +++ b/papers/F/Analysis/Real/Cloud/yamls/nautilus_real_cube_b2.yaml @@ -0,0 +1,84 @@ +# 21 processors on full for Varying F +# kubectl exec -it test-pod -- /bin/bash +apiVersion: batch/v1 +kind: Job +metadata: + name: jb-zdm-craco-real-logf-b2 +spec: + backoffLimit: 0 + template: + spec: + affinity: + nodeAffinity: + requiredDuringSchedulingIgnoredDuringExecution: + nodeSelectorTerms: + - matchExpressions: + - key: kubernetes.io/hostname + operator: NotIn + values: + - k8s-chase-ci-01.noc.ucsb.edu + - key: nvidia.com/gpu.product + operator: In + values: + - NVIDIA-GeForce-GTX-1080-Ti + containers: + - name: container + image: localhost:30081/profxj/zdm_docker:latest # UPDATE + imagePullPolicy: Always + resources: + requests: + cpu: "6" + memory: "64Gi" # + ephemeral-storage: 64Gi # + limits: + cpu: "8" + memory: "80Gi" + ephemeral-storage: 80Gi + #nvidia.com/gpu: "1" # See docs to exlude certain types + command: ["/bin/bash", "-c"] + args: + - cd FRB; + git fetch; + git pull; + python setup.py develop; + cd ../ne2001; + python setup.py develop; + cd ../zdm; + git fetch; + git checkout varying_F; + python setup.py develop; + cd zdm/data/Surveys; + aws --endpoint http://rook-ceph-rgw-nautiluss3.rook s3 cp s3://zdm/Surveys . --recursive --force; + cd ../../..; + cd papers/F/Analysis/Real/Cloud; + mkdir Output; + python run_real_craco_block.py -s 6 -e 10; + aws --endpoint http://rook-ceph-rgw-nautiluss3.rook s3 cp Output s3://zdm/Cubes/F/real/ --recursive --force; + env: + - name: "ENDPOINT_URL" + value: "http://rook-ceph-rgw-nautiluss3.rook" + - name: "S3_ENDPOINT" + value: "rook-ceph-rgw-nautiluss3.rook" + volumeMounts: + - name: prp-s3-credentials + mountPath: "/root/.aws/credentials" + subPath: "credentials" + - name: ephemeral + mountPath: "/tmp" + - name: "dshm" + mountPath: "/dev/shm" + nodeSelector: + nautilus.io/disktype: nvme + restartPolicy: Never + volumes: + # Secrets file for nautilus s3 credentials .aws/credentials and .s3cfg + - name: prp-s3-credentials + secret: + secretName: prp-s3-credentials + # Shared memory (necessary for Python's multiprocessing.shared_memory module to work) + - name: dshm + emptyDir: + medium: Memory + # Ephemeral storage + - name: ephemeral + emptyDir: {} diff --git a/papers/F/Analysis/Real/Cloud/yamls/nautilus_real_cube_b3.yaml b/papers/F/Analysis/Real/Cloud/yamls/nautilus_real_cube_b3.yaml new file mode 100644 index 00000000..9ac642a1 --- /dev/null +++ b/papers/F/Analysis/Real/Cloud/yamls/nautilus_real_cube_b3.yaml @@ -0,0 +1,84 @@ +# 21 processors on full for Varying F +# kubectl exec -it test-pod -- /bin/bash +apiVersion: batch/v1 +kind: Job +metadata: + name: jb-zdm-craco-real-logf-b3 +spec: + backoffLimit: 0 + template: + spec: + affinity: + nodeAffinity: + requiredDuringSchedulingIgnoredDuringExecution: + nodeSelectorTerms: + - matchExpressions: + - key: kubernetes.io/hostname + operator: NotIn + values: + - k8s-chase-ci-01.noc.ucsb.edu + - key: nvidia.com/gpu.product + operator: In + values: + - NVIDIA-GeForce-GTX-1080-Ti + containers: + - name: container + image: localhost:30081/profxj/zdm_docker:latest # UPDATE + imagePullPolicy: Always + resources: + requests: + cpu: "6" + memory: "64Gi" # + ephemeral-storage: 64Gi # + limits: + cpu: "8" + memory: "80Gi" + ephemeral-storage: 80Gi + #nvidia.com/gpu: "1" # See docs to exlude certain types + command: ["/bin/bash", "-c"] + args: + - cd FRB; + git fetch; + git pull; + python setup.py develop; + cd ../ne2001; + python setup.py develop; + cd ../zdm; + git fetch; + git checkout varying_F; + python setup.py develop; + cd zdm/data/Surveys; + aws --endpoint http://rook-ceph-rgw-nautiluss3.rook s3 cp s3://zdm/Surveys . --recursive --force; + cd ../../..; + cd papers/F/Analysis/Real/Cloud; + mkdir Output; + python run_real_craco_block.py -s 11 -e 15; + aws --endpoint http://rook-ceph-rgw-nautiluss3.rook s3 cp Output s3://zdm/Cubes/F/real/ --recursive --force; + env: + - name: "ENDPOINT_URL" + value: "http://rook-ceph-rgw-nautiluss3.rook" + - name: "S3_ENDPOINT" + value: "rook-ceph-rgw-nautiluss3.rook" + volumeMounts: + - name: prp-s3-credentials + mountPath: "/root/.aws/credentials" + subPath: "credentials" + - name: ephemeral + mountPath: "/tmp" + - name: "dshm" + mountPath: "/dev/shm" + nodeSelector: + nautilus.io/disktype: nvme + restartPolicy: Never + volumes: + # Secrets file for nautilus s3 credentials .aws/credentials and .s3cfg + - name: prp-s3-credentials + secret: + secretName: prp-s3-credentials + # Shared memory (necessary for Python's multiprocessing.shared_memory module to work) + - name: dshm + emptyDir: + medium: Memory + # Ephemeral storage + - name: ephemeral + emptyDir: {} diff --git a/papers/F/Analysis/Real/Cloud/yamls/nautilus_real_cube_b4.yaml b/papers/F/Analysis/Real/Cloud/yamls/nautilus_real_cube_b4.yaml new file mode 100644 index 00000000..0ce3cb1c --- /dev/null +++ b/papers/F/Analysis/Real/Cloud/yamls/nautilus_real_cube_b4.yaml @@ -0,0 +1,84 @@ +# 21 processors on full for Varying F +# kubectl exec -it test-pod -- /bin/bash +apiVersion: batch/v1 +kind: Job +metadata: + name: jb-zdm-craco-real-logf-b4 +spec: + backoffLimit: 0 + template: + spec: + affinity: + nodeAffinity: + requiredDuringSchedulingIgnoredDuringExecution: + nodeSelectorTerms: + - matchExpressions: + - key: kubernetes.io/hostname + operator: NotIn + values: + - k8s-chase-ci-01.noc.ucsb.edu + - key: nvidia.com/gpu.product + operator: In + values: + - NVIDIA-GeForce-GTX-1080-Ti + containers: + - name: container + image: localhost:30081/profxj/zdm_docker:latest # UPDATE + imagePullPolicy: Always + resources: + requests: + cpu: "6" + memory: "64Gi" # + ephemeral-storage: 64Gi # + limits: + cpu: "8" + memory: "80Gi" + ephemeral-storage: 80Gi + #nvidia.com/gpu: "1" # See docs to exlude certain types + command: ["/bin/bash", "-c"] + args: + - cd FRB; + git fetch; + git pull; + python setup.py develop; + cd ../ne2001; + python setup.py develop; + cd ../zdm; + git fetch; + git checkout varying_F; + python setup.py develop; + cd zdm/data/Surveys; + aws --endpoint http://rook-ceph-rgw-nautiluss3.rook s3 cp s3://zdm/Surveys . --recursive --force; + cd ../../..; + cd papers/F/Analysis/Real/Cloud; + mkdir Output; + python run_real_craco_block.py -s 16 -e 20; + aws --endpoint http://rook-ceph-rgw-nautiluss3.rook s3 cp Output s3://zdm/Cubes/F/real/ --recursive --force; + env: + - name: "ENDPOINT_URL" + value: "http://rook-ceph-rgw-nautiluss3.rook" + - name: "S3_ENDPOINT" + value: "rook-ceph-rgw-nautiluss3.rook" + volumeMounts: + - name: prp-s3-credentials + mountPath: "/root/.aws/credentials" + subPath: "credentials" + - name: ephemeral + mountPath: "/tmp" + - name: "dshm" + mountPath: "/dev/shm" + nodeSelector: + nautilus.io/disktype: nvme + restartPolicy: Never + volumes: + # Secrets file for nautilus s3 credentials .aws/credentials and .s3cfg + - name: prp-s3-credentials + secret: + secretName: prp-s3-credentials + # Shared memory (necessary for Python's multiprocessing.shared_memory module to work) + - name: dshm + emptyDir: + medium: Memory + # Ephemeral storage + - name: ephemeral + emptyDir: {} diff --git a/papers/F/Analysis/Real/Cloud/yamls/nautilus_real_cube_b5.yaml b/papers/F/Analysis/Real/Cloud/yamls/nautilus_real_cube_b5.yaml new file mode 100644 index 00000000..1d962539 --- /dev/null +++ b/papers/F/Analysis/Real/Cloud/yamls/nautilus_real_cube_b5.yaml @@ -0,0 +1,84 @@ +# 21 processors on full for Varying F +# kubectl exec -it test-pod -- /bin/bash +apiVersion: batch/v1 +kind: Job +metadata: + name: jb-zdm-craco-real-logf-b5 +spec: + backoffLimit: 0 + template: + spec: + affinity: + nodeAffinity: + requiredDuringSchedulingIgnoredDuringExecution: + nodeSelectorTerms: + - matchExpressions: + - key: kubernetes.io/hostname + operator: NotIn + values: + - k8s-chase-ci-01.noc.ucsb.edu + - key: nvidia.com/gpu.product + operator: In + values: + - NVIDIA-GeForce-GTX-1080-Ti + containers: + - name: container + image: localhost:30081/profxj/zdm_docker:latest # UPDATE + imagePullPolicy: Always + resources: + requests: + cpu: "6" + memory: "64Gi" # + ephemeral-storage: 64Gi # + limits: + cpu: "8" + memory: "80Gi" + ephemeral-storage: 80Gi + #nvidia.com/gpu: "1" # See docs to exlude certain types + command: ["/bin/bash", "-c"] + args: + - cd FRB; + git fetch; + git pull; + python setup.py develop; + cd ../ne2001; + python setup.py develop; + cd ../zdm; + git fetch; + git checkout varying_F; + python setup.py develop; + cd zdm/data/Surveys; + aws --endpoint http://rook-ceph-rgw-nautiluss3.rook s3 cp s3://zdm/Surveys . --recursive --force; + cd ../../..; + cd papers/F/Analysis/Real/Cloud; + mkdir Output; + python run_real_craco_block.py -s 21 -e 25; + aws --endpoint http://rook-ceph-rgw-nautiluss3.rook s3 cp Output s3://zdm/Cubes/F/real/ --recursive --force; + env: + - name: "ENDPOINT_URL" + value: "http://rook-ceph-rgw-nautiluss3.rook" + - name: "S3_ENDPOINT" + value: "rook-ceph-rgw-nautiluss3.rook" + volumeMounts: + - name: prp-s3-credentials + mountPath: "/root/.aws/credentials" + subPath: "credentials" + - name: ephemeral + mountPath: "/tmp" + - name: "dshm" + mountPath: "/dev/shm" + nodeSelector: + nautilus.io/disktype: nvme + restartPolicy: Never + volumes: + # Secrets file for nautilus s3 credentials .aws/credentials and .s3cfg + - name: prp-s3-credentials + secret: + secretName: prp-s3-credentials + # Shared memory (necessary for Python's multiprocessing.shared_memory module to work) + - name: dshm + emptyDir: + medium: Memory + # Ephemeral storage + - name: ephemeral + emptyDir: {} diff --git a/papers/F/Analysis/Real/Cloud/yamls/nautilus_real_cube_b6.yaml b/papers/F/Analysis/Real/Cloud/yamls/nautilus_real_cube_b6.yaml new file mode 100644 index 00000000..a7b2a806 --- /dev/null +++ b/papers/F/Analysis/Real/Cloud/yamls/nautilus_real_cube_b6.yaml @@ -0,0 +1,84 @@ +# 21 processors on full for Varying F +# kubectl exec -it test-pod -- /bin/bash +apiVersion: batch/v1 +kind: Job +metadata: + name: jb-zdm-craco-real-logf-b6 +spec: + backoffLimit: 0 + template: + spec: + affinity: + nodeAffinity: + requiredDuringSchedulingIgnoredDuringExecution: + nodeSelectorTerms: + - matchExpressions: + - key: kubernetes.io/hostname + operator: NotIn + values: + - k8s-chase-ci-01.noc.ucsb.edu + - key: nvidia.com/gpu.product + operator: In + values: + - NVIDIA-GeForce-GTX-1080-Ti + containers: + - name: container + image: localhost:30081/profxj/zdm_docker:latest # UPDATE + imagePullPolicy: Always + resources: + requests: + cpu: "6" + memory: "64Gi" # + ephemeral-storage: 64Gi # + limits: + cpu: "8" + memory: "80Gi" + ephemeral-storage: 80Gi + #nvidia.com/gpu: "1" # See docs to exlude certain types + command: ["/bin/bash", "-c"] + args: + - cd FRB; + git fetch; + git pull; + python setup.py develop; + cd ../ne2001; + python setup.py develop; + cd ../zdm; + git fetch; + git checkout varying_F; + python setup.py develop; + cd zdm/data/Surveys; + aws --endpoint http://rook-ceph-rgw-nautiluss3.rook s3 cp s3://zdm/Surveys . --recursive --force; + cd ../../..; + cd papers/F/Analysis/Real/Cloud; + mkdir Output; + python run_real_craco_block.py -s 26 -e 30; + aws --endpoint http://rook-ceph-rgw-nautiluss3.rook s3 cp Output s3://zdm/Cubes/F/real/ --recursive --force; + env: + - name: "ENDPOINT_URL" + value: "http://rook-ceph-rgw-nautiluss3.rook" + - name: "S3_ENDPOINT" + value: "rook-ceph-rgw-nautiluss3.rook" + volumeMounts: + - name: prp-s3-credentials + mountPath: "/root/.aws/credentials" + subPath: "credentials" + - name: ephemeral + mountPath: "/tmp" + - name: "dshm" + mountPath: "/dev/shm" + nodeSelector: + nautilus.io/disktype: nvme + restartPolicy: Never + volumes: + # Secrets file for nautilus s3 credentials .aws/credentials and .s3cfg + - name: prp-s3-credentials + secret: + secretName: prp-s3-credentials + # Shared memory (necessary for Python's multiprocessing.shared_memory module to work) + - name: dshm + emptyDir: + medium: Memory + # Ephemeral storage + - name: ephemeral + emptyDir: {} diff --git a/papers/F/Analysis/Real/Cloud/yamls/nautilus_real_cube_b7.yaml b/papers/F/Analysis/Real/Cloud/yamls/nautilus_real_cube_b7.yaml new file mode 100644 index 00000000..b21430f5 --- /dev/null +++ b/papers/F/Analysis/Real/Cloud/yamls/nautilus_real_cube_b7.yaml @@ -0,0 +1,84 @@ +# 21 processors on full for Varying F +# kubectl exec -it test-pod -- /bin/bash +apiVersion: batch/v1 +kind: Job +metadata: + name: jb-zdm-craco-real-logf-b7 +spec: + backoffLimit: 0 + template: + spec: + affinity: + nodeAffinity: + requiredDuringSchedulingIgnoredDuringExecution: + nodeSelectorTerms: + - matchExpressions: + - key: kubernetes.io/hostname + operator: NotIn + values: + - k8s-chase-ci-01.noc.ucsb.edu + - key: nvidia.com/gpu.product + operator: In + values: + - NVIDIA-GeForce-GTX-1080-Ti + containers: + - name: container + image: localhost:30081/profxj/zdm_docker:latest # UPDATE + imagePullPolicy: Always + resources: + requests: + cpu: "6" + memory: "64Gi" # + ephemeral-storage: 64Gi # + limits: + cpu: "8" + memory: "80Gi" + ephemeral-storage: 80Gi + #nvidia.com/gpu: "1" # See docs to exlude certain types + command: ["/bin/bash", "-c"] + args: + - cd FRB; + git fetch; + git pull; + python setup.py develop; + cd ../ne2001; + python setup.py develop; + cd ../zdm; + git fetch; + git checkout varying_F; + python setup.py develop; + cd zdm/data/Surveys; + aws --endpoint http://rook-ceph-rgw-nautiluss3.rook s3 cp s3://zdm/Surveys . --recursive --force; + cd ../../..; + cd papers/F/Analysis/Real/Cloud; + mkdir Output; + python run_real_craco_block.py -s 31 -e 35; + aws --endpoint http://rook-ceph-rgw-nautiluss3.rook s3 cp Output s3://zdm/Cubes/F/real/ --recursive --force; + env: + - name: "ENDPOINT_URL" + value: "http://rook-ceph-rgw-nautiluss3.rook" + - name: "S3_ENDPOINT" + value: "rook-ceph-rgw-nautiluss3.rook" + volumeMounts: + - name: prp-s3-credentials + mountPath: "/root/.aws/credentials" + subPath: "credentials" + - name: ephemeral + mountPath: "/tmp" + - name: "dshm" + mountPath: "/dev/shm" + nodeSelector: + nautilus.io/disktype: nvme + restartPolicy: Never + volumes: + # Secrets file for nautilus s3 credentials .aws/credentials and .s3cfg + - name: prp-s3-credentials + secret: + secretName: prp-s3-credentials + # Shared memory (necessary for Python's multiprocessing.shared_memory module to work) + - name: dshm + emptyDir: + medium: Memory + # Ephemeral storage + - name: ephemeral + emptyDir: {} diff --git a/papers/F/Analysis/Real/Cloud/yamls/nautilus_real_cube_b8.yaml b/papers/F/Analysis/Real/Cloud/yamls/nautilus_real_cube_b8.yaml new file mode 100644 index 00000000..00396b5c --- /dev/null +++ b/papers/F/Analysis/Real/Cloud/yamls/nautilus_real_cube_b8.yaml @@ -0,0 +1,84 @@ +# 21 processors on full for Varying F +# kubectl exec -it test-pod -- /bin/bash +apiVersion: batch/v1 +kind: Job +metadata: + name: jb-zdm-craco-real-logf-b8 +spec: + backoffLimit: 0 + template: + spec: + affinity: + nodeAffinity: + requiredDuringSchedulingIgnoredDuringExecution: + nodeSelectorTerms: + - matchExpressions: + - key: kubernetes.io/hostname + operator: NotIn + values: + - k8s-chase-ci-01.noc.ucsb.edu + - key: nvidia.com/gpu.product + operator: In + values: + - NVIDIA-GeForce-GTX-1080-Ti + containers: + - name: container + image: localhost:30081/profxj/zdm_docker:latest # UPDATE + imagePullPolicy: Always + resources: + requests: + cpu: "6" + memory: "64Gi" # + ephemeral-storage: 64Gi # + limits: + cpu: "8" + memory: "80Gi" + ephemeral-storage: 80Gi + #nvidia.com/gpu: "1" # See docs to exlude certain types + command: ["/bin/bash", "-c"] + args: + - cd FRB; + git fetch; + git pull; + python setup.py develop; + cd ../ne2001; + python setup.py develop; + cd ../zdm; + git fetch; + git checkout varying_F; + python setup.py develop; + cd zdm/data/Surveys; + aws --endpoint http://rook-ceph-rgw-nautiluss3.rook s3 cp s3://zdm/Surveys . --recursive --force; + cd ../../..; + cd papers/F/Analysis/Real/Cloud; + mkdir Output; + python run_real_craco_block.py -s 36 -e 41; + aws --endpoint http://rook-ceph-rgw-nautiluss3.rook s3 cp Output s3://zdm/Cubes/F/real/ --recursive --force; + env: + - name: "ENDPOINT_URL" + value: "http://rook-ceph-rgw-nautiluss3.rook" + - name: "S3_ENDPOINT" + value: "rook-ceph-rgw-nautiluss3.rook" + volumeMounts: + - name: prp-s3-credentials + mountPath: "/root/.aws/credentials" + subPath: "credentials" + - name: ephemeral + mountPath: "/tmp" + - name: "dshm" + mountPath: "/dev/shm" + nodeSelector: + nautilus.io/disktype: nvme + restartPolicy: Never + volumes: + # Secrets file for nautilus s3 credentials .aws/credentials and .s3cfg + - name: prp-s3-credentials + secret: + secretName: prp-s3-credentials + # Shared memory (necessary for Python's multiprocessing.shared_memory module to work) + - name: dshm + emptyDir: + medium: Memory + # Ephemeral storage + - name: ephemeral + emptyDir: {} diff --git a/papers/F/Analysis/Real/cube_diag.ipynb b/papers/F/Analysis/Real/cube_diag.ipynb new file mode 100644 index 00000000..9f31e45c --- /dev/null +++ b/papers/F/Analysis/Real/cube_diag.ipynb @@ -0,0 +1,1377 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import zdm.analyze_cube as ac\n", + "import matplotlib.pyplot as plt\n", + "import zdm.analyze_cube as ac" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## inspecting cubes" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "cube_dir = \"../CRACO/Cubes/craco_full_cube.npz\"\n", + "cube_dir_real =\"./Cubes/craco_real_cube.npz\"\n", + "\n", + "cube=np.load(cube_dir)\n", + "cube_real=np.load(cube_dir_real)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Marginalization over H0\n", + "fig, ax = plt.subplots(5,5, dpi=200, figsize=(15,15))\n", + "\n", + "H0s = np.linspace(75, 100, 25)\n", + "\n", + "for H0, a in zip(H0s, ax.flatten()):\n", + " ll = ac.get_slice_from_parameters(cube, [\"H0\", \"lmean\", \"lsigma\"], [H0, 2.16, .51], wanted=\"ll\")\n", + " ll_real = ac.get_slice_from_parameters(cube_real, [\"H0\", \"lmean\", \"lsigma\"], [H0, 2.16, .51], wanted=\"ll\")\n", + "\n", + " \n", + " _, vectors, _= ac.get_bayesian_data(ll)\n", + " _, vectors_real, _ = ac.get_bayesian_data(ll_real)\n", + "\n", + " ll[np.isnan(ll)] = -1e99\n", + " ll -= np.max(ll)\n", + " ll = 10**ll\n", + " ll /= np.sum(ll)\n", + " ll_real[np.isnan(ll_real)] = -1e99\n", + " ll_real -= np.max(ll_real)\n", + " ll_real = 10**ll_real\n", + " ll_real /= np.sum(ll_real)\n", + "\n", + " a.plot(cube[\"logF\"], vectors[-1], c=\"b\", label=\"Synth\")\n", + " a.plot(cube[\"logF\"], vectors_real[-1], c=\"r\", label=\"Real\") \n", + "\n", + " a.plot(cube[\"logF\"], ll, c=\"b\", ls='--', alpha=.5)\n", + " a.plot(cube[\"logF\"], ll_real, c=\"r\", ls='--', alpha=.5) \n", + "\n", + " a.set_xlabel(\"log F\")\n", + " a.set_ylabel(\"ll\")\n", + " a.text(.05, .925,f\"H_0 = {np.round(H0,3)}\", transform=a.transAxes)\n", + "\n", + " if H0 == H0s[0]:\n", + " a.legend(loc=\"lower left\")\n", + "\n", + "fig.suptitle(\"bayesian\")\n", + "fig.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(5, 5, dpi=200, figsize=(15,15))\n", + "\n", + "for H0, a in zip(H0s, ax.flatten()):\n", + " ll = ac.get_slice_from_parameters(cube, [\"H0\", \"lmean\", \"lsigma\"], [H0, 2.16, .51], wanted=\"ll\")\n", + "\n", + " ll[np.isnan(ll)] = -1e99\n", + " ll -= np.max(ll)\n", + " ll = 10**ll\n", + " ll /= np.sum(ll)\n", + "\n", + " ll_real = ac.get_slice_from_parameters(cube_real, [\"H0\", \"lmean\", \"lsigma\"], [H0, 2.16, .51], wanted=\"ll\")\n", + " ll_real[np.isnan(ll_real)] = -1e99\n", + " ll_real -= np.max(ll_real)\n", + " ll_real = 10**ll_real\n", + " ll_real /= np.sum(ll_real)\n", + "\n", + " a.plot(cube[\"logF\"], ll, c=\"b\")\n", + " a.plot(cube[\"logF\"], ll_real, c=\"r\")\n", + " \n", + " a.set_xlabel(\"log F\")\n", + " a.set_ylabel(\"ll\")\n", + " a.text(.05, .925,f\"H_0 = {np.round(H0,3)}\", transform=a.transAxes)\n", + "\n", + "fig.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "H0_diag = 65\n", + "\n", + "lmeans = cube[\"lmean\"]\n", + "\n", + "fig, ax = plt.subplots(2, 5, dpi=200, figsize=(15,5))\n", + "\n", + "\n", + "for lmean, a in zip(lmeans, ax.flatten()):\n", + " ll = ac.get_slice_from_parameters(cube, [\"H0\", \"lmean\", \"lsigma\"], [H0_diag, lmean, .51], wanted=\"ll\")\n", + " ll[np.isnan(ll)] = -1e99\n", + " ll -= np.max(ll)\n", + " ll = 10**ll\n", + " ll /= np.sum(ll)\n", + "\n", + " ll_real = ac.get_slice_from_parameters(cube_real, [\"H0\", \"lmean\", \"lsigma\"], [H0_diag, lmean, .51], wanted=\"ll\")\n", + " ll_real[np.isnan(ll_real)] = -1e99\n", + " ll_real -= np.max(ll_real)\n", + " ll_real = 10**ll_real\n", + " ll_real /= np.sum(ll_real)\n", + "\n", + " a.plot(cube[\"logF\"], ll, c=\"b\", label=\"Synth\")\n", + " a.plot(cube[\"logF\"], ll_real, c=\"r\", label=\"Real\")\n", + " \n", + " a.set_xlabel(\"log F\")\n", + " a.set_ylabel(\"ll\")\n", + " a.text(.05, .925,f\"lmean = {np.round(lmean,3)}\", transform=a.transAxes)\n", + "\n", + " if lmean == lmeans[0]:\n", + " a.legend(loc=\"lower left\")\n", + "\n", + "fig.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sigmas = cube[\"lsigma\"]\n", + "\n", + "fig, ax = plt.subplots(2, 5, dpi=200, figsize=(15,5))\n", + "\n", + "for sigma, a in zip(sigmas, ax.flatten()):\n", + " ll = ac.get_slice_from_parameters(cube, [\"H0\", \"lmean\", \"lsigma\"], [H0_diag, 2.16, sigma], wanted=\"ll\")\n", + " ll[np.isnan(ll)] = -1e99\n", + " ll -= np.max(ll)\n", + " ll = 10**ll\n", + " ll /= np.sum(ll)\n", + "\n", + " ll_real = ac.get_slice_from_parameters(cube_real, [\"H0\", \"lmean\", \"lsigma\"], [H0_diag, 2.16, sigma], wanted=\"ll\")\n", + " ll_real[np.isnan(ll_real)] = -1e99\n", + " ll_real -= np.max(ll_real)\n", + " ll_real = 10**ll_real\n", + " ll_real /= np.sum(ll_real)\n", + "\n", + " a.plot(cube[\"logF\"], ll, c=\"b\", label=\"Synth\")\n", + " a.plot(cube[\"logF\"], ll_real, c=\"r\", label=\"Real\")\n", + " \n", + " a.set_xlabel(\"log F\")\n", + " a.set_ylabel(\"ll\")\n", + " a.text(.05, .925,f\"lsigma = {np.round(sigma,3)}\", transform=a.transAxes)\n", + "\n", + " if sigma == sigmas[0]:\n", + " a.legend(loc=\"lower left\")\n", + "\n", + "fig.tight_layout()\n", + "plt.show()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## looking at the csvs" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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nH0lmeanlsigmalogFlClls0P_zDM0P_n0P_s0...P_s4N4llsP_zDMP_nP_sp_zgDMp_DMp_DMgzp_z
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..................................................................
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200 rows × 39 columns

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The slices +obtained are set at the best-fit values of cube parameters. + +""" + +import numpy as np +import os +import zdm +from zdm import analyze_cube as ac + +from matplotlib import pyplot as plt + +def main(verbose=False): + + # output directory + opdir="Figure9/" + if not os.path.exists(opdir): + os.mkdir(opdir) + + CubeFile='Cubes/craco_real_cube.npz' + if os.path.exists(CubeFile): + data=np.load(CubeFile) + else: + print("Could not file cube output file ",CubeFile) + print("Please obtain it from [repository]") + exit() + + if verbose: + print("Data file contains the following items") + for thing in data: + print(thing) + + lst = data.files + lldata=data["ll"] + params=data["params"] + + def get_param_values(data,params): + """ + Gets the unique values of the data from a cube output + Currently the parameter order is hard-coded + + """ + param_vals=[] + for param in params: + col=data[param] + unique=np.unique(col) + param_vals.append(unique) + return param_vals + + param_vals=get_param_values(data, params) + + + #reconstructs total pdmz given all the pieces, including unlocalised contributions + pDMz=data["P_zDM0"]+data["P_zDM1"]+data["P_zDM2"]+data["P_zDM3"]+data["P_zDM4"] + + #DM only contribution - however it ignores unlocalised DMs from surveys 1-3 + pDMonly=data["pDM"]+data["P_zDM0"]+data["P_zDM4"] + + #do this over all surveys + P_s=data["P_s0"]+data["P_s1"]+data["P_s2"]+data["P_s3"]+data["P_s4"] + P_n=data["P_n0"]+data["P_n1"]+data["P_n2"]+data["P_n3"]+data["P_n4"] + + #labels=['p(N,s,DM,z)','P_n','P(s|DM,z)','p(DM,z)all','p(DM)all','p(z|DM)','p(DM)','p(DM|z)','p(z)'] + #for datatype in [data["ll"],P_n,P_s,pDMz,pDMonly,data["pzDM"],data["pDM"],data["pDMz"],data["pz"]]: + + # builds uvals list + uvals=[] + latexnames=[] + for ip,param in enumerate(data["params"]): + # switches for alpha + if param=="alpha": + uvals.append(data[param]*-1.) + else: + uvals.append(data[param]) + if param=="alpha": + latexnames.append('$\\alpha$') + ialpha=ip + elif param=="lEmax": + latexnames.append('$\\log_{10} E_{\\rm max}$') + elif param=="H0": + latexnames.append('$H_0$') + elif param=="gamma": + latexnames.append('$\\gamma$') + elif param=="sfr_n": + latexnames.append('$n_{\\rm sfr}$') + elif param=="lmean": + latexnames.append('$\\mu_{\\rm host}$') + elif param=="lsigma": + latexnames.append('$\\sigma_{\\rm host}$') + elif param=="logF": + latexnames.append('$\\log_{10} F$') + + #latexnames=['$\\log_{10} E_{\\rm max}$','$H_0$','$\\alpha$','$\\gamma$','$n_{\\rm sfr}$','$\\mu_{\\rm host}$','$\\sigma_{\\rm host}$'] + + list2=[] + vals2=[] + # gets Bayesian posteriors + deprecated,uw_vectors,wvectors=ac.get_bayesian_data(data["ll"]) + for i,vec in enumerate(uw_vectors): + n=np.argmax(vec) + val=uvals[i][n] + if params[i] != "logF": + list2.append(params[i]) + vals2.append(val) + else: + iF=i + + ###### NOTATION ##### + # uw: unweighted + # wH0: weighted according to H0 knowledged + # f: fixed other parameters + # B: best-fit + + ############## 2D plots at best-fit valuess ########## + + # gets the slice corresponding to the best-fit values of all other parameters + # this is 1D, so is our limit on H0 keeping all others fixed + for i,item in enumerate(list2): + + list3=np.concatenate((list2[0:i],list2[i+1:])) + vals3=np.concatenate((vals2[0:i],vals2[i+1:])) + array=ac.get_slice_from_parameters(data,list3,vals3) + + # log to lin space + array -= np.max(array) + array = 10**array + array /= np.sum(array) + + # now have array for slice covering best-fit values + if i < iF: + modi=i + else: + modi=i+1 + #array=array.T + array=array.swapaxes(0,1) + savename=opdir+"/lls_"+params[iF]+"_"+params[modi]+".png" + + if params[modi]=="alpha": + #switches order of array in alpha dimension + array=np.flip(array,axis=0) + ac.make_2d_plot(array,latexnames[modi],latexnames[iF], + -param_vals[modi],param_vals[iF], + savename=savename,norm=1) + else: + ac.make_2d_plot(array,latexnames[modi],latexnames[iF], + param_vals[modi],param_vals[iF], + savename=savename,norm=1) + +main() diff --git a/papers/F/Analysis/Real/test.py b/papers/F/Analysis/Real/test.py new file mode 100644 index 00000000..ede07066 --- /dev/null +++ b/papers/F/Analysis/Real/test.py @@ -0,0 +1,152 @@ +import numpy as np +import os +import zdm +from zdm import analyze_cube as ac + +from matplotlib import pyplot as plt +from IPython import embed + + +def main(verbose=False): + + # output directory + opdir = "figs/" + if not os.path.exists(opdir): + os.mkdir(opdir) + + CubeFile = "Cubes/craco_real_cube.npz" + if os.path.exists(CubeFile): + data = np.load(CubeFile) + else: + print("Could not file cube output file ", CubeFile) + print("Please obtain it from [repository]") + exit() + + if verbose: + print("Data file contains the following items") + for thing in data: + print(thing) + + lst = data.files + lldata = data["ll"] + params = data["params"] + + def get_param_values(data, params): + """ + Gets the unique values of the data from a cube output + Currently the parameter order is hard-coded + + """ + param_vals = [] + for param in params: + col = data[param] + unique = np.unique(col) + param_vals.append(unique) + return param_vals + + param_vals = get_param_values(data, params) + + # builds uvals list + uvals = [] + latexnames = [] + for ip, param in enumerate(data["params"]): + # switches for alpha + if param == "alpha": + uvals.append(data[param] * -1.0) + else: + uvals.append(data[param]) + if param == "alpha": + latexnames.append("$\\alpha$") + ialpha = ip + elif param == "lEmax": + latexnames.append("$\\log_{10} E_{\\rm max}$") + elif param == "H0": + latexnames.append("$H_0$") + elif param == "gamma": + latexnames.append("$\\gamma$") + elif param == "sfr_n": + latexnames.append("$n_{\\rm sfr}$") + elif param == "lmean": + latexnames.append("$\\mu_{\\rm host}$") + elif param == "lsigma": + latexnames.append("$\\sigma_{\\rm host}$") + elif param == "logF": + latexnames.append("$\\log_{10} F$") + + # latexnames=['$\\log_{10} E_{\\rm max}$','$H_0$','$\\alpha$','$\\gamma$','$n_{\\rm sfr}$','$\\mu_{\\rm host}$','$\\sigma_{\\rm host}$'] + + list2 = [] + vals2 = [] + # gets Bayesian posteriors + deprecated, uw_vectors, wvectors = ac.get_bayesian_data(data["ll"]) + for i, vec in enumerate(uw_vectors): + n = np.argmax(vec) + val = uvals[i][n] + if params[i] != "H0": + list2.append(params[i]) + vals2.append(val) + else: + iH0 = i + + ###### NOTATION ##### + # uw: unweighted + # wH0: weighted according to H0 knowledged + # f: fixed other parameters + # B: best-fit + + ############## 2D plots at best-fit valuess ########## + + # gets the slice corresponding to the best-fit values of all other parameters + # this is 1D, so is our limit on H0 keeping all others fixed + for i, item in enumerate(list2): + + list3 = np.concatenate((list2[0:i], list2[i + 1 :])) + vals3 = np.concatenate((vals2[0:i], vals2[i + 1 :])) + array = ac.get_slice_from_parameters(data, list3, vals3) + + # log to lin space + array[np.isnan(array)] = -1e99 + array -= np.max(array) + array = 10 ** array + array /= np.sum(array) + + # now have array for slice covering best-fit values + if i < iH0: + modi = i + else: + modi = i + 1 + # array=array.T + array = array.swapaxes(0, 1) + savename = opdir + "/lls_" + params[iH0] + "_" + params[modi] + ".png" + + # if (latexnames[modi] == '$\\gamma$'): + # embed(header="gamma") + + # if (latexnames[modi] == '$H_0$'): + # embed(header="H0") + + if params[modi] == "alpha": + # switches order of array in alpha dimension + array = np.flip(array, axis=0) + ac.make_2d_plot( + array, + latexnames[modi], + latexnames[iH0], + -param_vals[modi], + param_vals[iH0], + savename=savename, + norm=1, + ) + else: + ac.make_2d_plot( + array, + latexnames[modi], + latexnames[iH0], + param_vals[modi], + param_vals[iH0], + savename=savename, + norm=1, + ) + + +main() diff --git a/papers/F/Analysis/Real/testF.py b/papers/F/Analysis/Real/testF.py index b253d0e1..a2804bf0 100644 --- a/papers/F/Analysis/Real/testF.py +++ b/papers/F/Analysis/Real/testF.py @@ -105,7 +105,7 @@ def get_param_values(data,params): # log to lin space array[np.isnan(array)] = -1e99 - array -= np.max(array) + array -= np.nanmax(array) array = 10**array array /= np.sum(array) diff --git a/papers/F/Analysis/Real/testing_bayesian.ipynb b/papers/F/Analysis/Real/testing_bayesian.ipynb new file mode 100644 index 00000000..914e2a08 --- /dev/null +++ b/papers/F/Analysis/Real/testing_bayesian.ipynb @@ -0,0 +1,186 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import zdm.analyze_cube as ac" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "cube_dir = \"../CRACO/Cubes/craco_full_cube.npz\"\n", + "cube=np.load(cube_dir)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "lls = ac.get_slice_from_parameters(cube, [\"H0\", \"lmean\", \"lsigma\"], [70, 2.16, .51], wanted=\"ll\")\n", + "global_max = np.nanmax(lls)\n", + "lls -= global_max\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "NDIMS = len(lls.shape)\n", + "big_slice = [slice(None, None, None)] * NDIMS" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "test_lls = lls[tuple(big_slice)].flatten()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-7.4237268e+02, -5.7061609e+02, -4.3890594e+02, -3.3762085e+02,\n", + " -2.5949152e+02, -1.9904089e+02, -1.5214069e+02, -1.1566675e+02,\n", + " -8.7230286e+01, -6.4988464e+01, -4.7544739e+01, -3.3898193e+01,\n", + " -2.3339355e+01, -1.5324219e+01, -9.4163818e+00, -5.2522583e+00,\n", + " -2.5090942e+00, -8.9019775e-01, -1.2945557e-01, 0.0000000e+00,\n", + " -3.1677246e-01, -9.3414307e-01, -1.7406006e+00, -2.6526489e+00,\n", + " -3.6091919e+00, -4.5662231e+00, -5.4932251e+00, -6.3695679e+00,\n", + " -7.1822510e+00, -7.9241333e+00], dtype=float32)" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test_lls" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "ignore = np.where(test_lls == 0.0)[0]\n", + "test_lls[ignore] = -99999" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-7.4237268e+02, -5.7061609e+02, -4.3890594e+02, -3.3762085e+02,\n", + " -2.5949152e+02, -1.9904089e+02, -1.5214069e+02, -1.1566675e+02,\n", + " -8.7230286e+01, -6.4988464e+01, -4.7544739e+01, -3.3898193e+01,\n", + " -2.3339355e+01, -1.5324219e+01, -9.4163818e+00, -5.2522583e+00,\n", + " -2.5090942e+00, -8.9019775e-01, -1.2945557e-01, -9.9999000e+04,\n", + " -3.1677246e-01, -9.3414307e-01, -1.7406006e+00, -2.6526489e+00,\n", + " -3.6091919e+00, -4.5662231e+00, -5.4932251e+00, -6.3695679e+00,\n", + " -7.1822510e+00, -7.9241333e+00], dtype=float32)" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lls" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ True, True, True, True, True, True, True, True, True,\n", + " True, True, True, True, True, True, True, True, True,\n", + " True, True, True, True, True, True, True, True, True,\n", + " True, True, True])" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test_lls == lls" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "themax = np.nanmax(lls)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "OKlls = np.isfinite(lls) & (lls > themax - 3)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "9731a3b7ebb41545a410367c249afbc4842fd5740da82f1bd29e6ac1d7253e6e" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/papers/F/Analysis/Real/tmp.ipynb b/papers/F/Analysis/Real/tmp.ipynb deleted file mode 100644 index de94906f..00000000 --- a/papers/F/Analysis/Real/tmp.ipynb +++ /dev/null @@ -1,150 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import zdm.analyze_cube as ac\n", - "import matplotlib.pyplot as plt\n", - "import zdm.analyze_cube as ac" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "cube_dir = \"./Cubes/craco_real_cube.npz\"\n", - "\n", - "cube=np.load(cube_dir)\n", - "ll = ac.get_slice_from_parameters(cube, [\"H0\"], [74], wanted=\"ll\")\n", - "_, vectors, _= ac.get_bayesian_data(ll)\n", - "plt.plot(cube[\"logF\"], vectors[-1])" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.0" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.max(ll.flatten())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "cube_dir = \"../CRACO/Cubes/craco_full_cube.npz\"\n", - "cube=np.load(cube_dir)\n", - "ll = ac.get_slice_from_parameters(cube, [\"H0\"], [74], wanted=\"ll\")\n", - "_, vectors, _= ac.get_bayesian_data(ll)\n", - "plt.plot(cube[\"logF\"], vectors[-1])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.8.5 ('base')", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5 (default, Sep 4 2020, 02:22:02) \n[Clang 10.0.0 ]" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "9731a3b7ebb41545a410367c249afbc4842fd5740da82f1bd29e6ac1d7253e6e" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/papers/F/Analysis/py/get_PDFs.py b/papers/F/Analysis/py/get_PDFs.py new file mode 100644 index 00000000..cb7d7b6b --- /dev/null +++ b/papers/F/Analysis/py/get_PDFs.py @@ -0,0 +1,160 @@ +import numpy as np +import os +import zdm +import scipy +from zdm import analyze_cube as ac +from IPython import embed +import matplotlib.pyplot as plt + + +def main(): + # cube_old = "../Real/Cubes/craco_real_old_cube.npz" + cube_old = "../CRACO/Cubes/craco_full_cube.npz" + cube = "../Real/Cubes/craco_real_cube.npz" + + # old cube + funcs, interp_mins, interp_maxs = getlogPDFs(cube_old) + _, _, _, flogF = funcs + _, _, _, logF_min = interp_mins + _, _, _, logF_max = interp_maxs + res = 1e3 + thresh = 1e-3 + logFs = np.linspace(logF_min, logF_max, int(res)) + + probs_old = np.exp(flogF(logFs)) + max_prob_old = np.max(probs_old) + max_F_old = logFs[np.argmax(probs_old)] + + sort_idx_old = np.argsort(np.abs(probs_old - (max_prob_old / 2))) + half_max_Fs_old = logFs[sort_idx_old[1]] + + # new cube + funcs, interp_mins, interp_maxs = getlogPDFs(cube) + _, _, _, flogF = funcs + _, _, _, logF_min = interp_mins + _, _, _, logF_max = interp_maxs + + probs = np.exp(flogF(logFs)) + max_prob = np.max(probs) + max_F = logFs[np.argmax(probs)] + + sort_idx = np.argsort(np.abs(probs - (max_prob / 2))) + half_max_Fs = logFs[sort_idx[3]] + + print("debugging", logFs[sort_idx]) + + # Left-sided half width half max + print( + "Left-sided half-width half-max for old cube: ", + np.abs(max_F_old - np.min(half_max_Fs_old)), + ) + print( + "Left-sided half-width half-max for new cube: ", + np.abs(max_F - np.min(half_max_Fs)), + ) + + plt.figure() + # old cube + plt.plot(logFs, probs_old, color="red", label="Before 2022 FRBs") + plt.scatter(half_max_Fs_old, probs_old[sort_idx_old[1]]) + # plt.scatter(max_F_old, max_prob_old) + plt.axhline(max_prob_old / 2, color="red", alpha=0.5, ls="--") + + # new cube + + plt.plot(logFs, probs, color="blue", label="After 2022 FRBs") + plt.scatter(half_max_Fs, probs[sort_idx[3]]) + # plt.scatter(max_F, max_prob) + plt.axhline(max_prob / 2, color="blue", alpha=0.5, ls="--") + + plt.legend() + + plt.xlabel(r"$\log_{10} F$") + plt.ylabel(r"$p(\log_{10} F)$") + + plt.text( + 0.05, + 0.75, + f"LWHM (old) = {np.round(np.abs(max_F_old - np.min(half_max_Fs_old)), 3)} \ + \nLWHM (new) = {np.round(np.abs(max_F - np.min(half_max_Fs)), 3)}", + transform=plt.gca().transAxes, + ) + + plt.savefig("diagnostic_2.png") + plt.close() + + # embed(header="end of main") + # + + +def getlogPDFs(cube): + ######### sets the values of H0 for priors ##### + Planck_H0 = 67.4 + Planck_sigma = 0.5 + Reiss_H0 = 73.04 + Reiss_sigma = 1.42 + ######### + + data = np.load(cube) + + uvals, latexnames = get_names_values(data) + + H0_dim = np.where(data["params"] == "H0")[0][0] + wlls = ac.apply_H0_prior( + data["ll"], H0_dim, data["H0"], Planck_H0, Planck_sigma, Reiss_H0, Reiss_sigma + ) + deprecated, wH0_vectors, wvectors = ac.get_bayesian_data(wlls) + + nonnorm_pdfs = [] + pdf_mins = [] + pdf_maxs = [] + + for i, vals in enumerate(uvals): + ymax = np.max(wH0_vectors[i]) + temp = np.where((wH0_vectors[i] > 0.0) & (np.isfinite(wH0_vectors[i]))) + + f = scipy.interpolate.interp1d( + vals[temp], np.log(wH0_vectors[i][temp]), kind="cubic" + ) + # remember to exponentiate output from f + nonnorm_pdfs.append(f) + pdf_mins.append(np.min(vals[temp])) + pdf_maxs.append(np.max(vals[temp])) + + return nonnorm_pdfs, pdf_mins, pdf_maxs + + +def get_names_values(data): + """ + Gets a list of latex names and corrected parameter values + """ + # builds uvals list + uvals = [] + latexnames = [] + for ip, param in enumerate(data["params"]): + # switches for alpha + if param == "alpha": + uvals.append(data[param] * -1.0) + else: + uvals.append(data[param]) + if param == "alpha": + latexnames.append("$\\alpha$") + ialpha = ip + elif param == "lEmax": + latexnames.append("$\\log_{10} E_{\\rm max}$") + elif param == "H0": + latexnames.append("$H_0$") + elif param == "gamma": + latexnames.append("$\\gamma$") + elif param == "sfr_n": + latexnames.append("$n_{\\rm sfr}$") + elif param == "lmean": + latexnames.append("$\\mu_{\\rm host}$") + elif param == "lsigma": + latexnames.append("$\\sigma_{\\rm host}$") + elif param == "logF": + latexnames.append("$\\log F$") + return uvals, latexnames + + +main() diff --git a/papers/F/Analysis/py/plot_limits_from_cube.py b/papers/F/Analysis/py/plot_limits_from_cube.py new file mode 100644 index 00000000..1ee97172 --- /dev/null +++ b/papers/F/Analysis/py/plot_limits_from_cube.py @@ -0,0 +1,193 @@ +""" +This is a script to produce limit plots for a cube with priors on F + +It produces two sets of plots: +- single parameter limits also showing results with: + a) a Gaussian prior + b) No prior + +It also collects data to plot a result on F for best-fit values of all +other parameters, but currently does not produce that plot + +""" + +import numpy as np +import os +import zdm +from zdm import analyze_cube as ac + +from matplotlib import pyplot as plt + + +def main(verbose=False): + ######### sets the values of F for priors ##### + F_0 = np.log10(0.32) + F_sigma = np.abs(0.2 * F_0) # error of 20% on F + + ##### loads cube data ##### + cube = "../Real/Cubes/craco_real_cube.npz" + # cube = "../CRACO/Cubes/craco_full_cube.npz" + + data = np.load(cube) + if verbose: + for thing in data: + print(thing) + print(data["params"]) + + # gets values of cube parameters + # param_vals=get_param_values(data,verbose) + + # gets latex names + uvals, latexnames = get_names_values(data) + + ################ single plots, no priors ############ + deprecated, uw_vectors, wvectors = ac.get_bayesian_data(data["ll"]) + ac.do_single_plots( + uvals, + uw_vectors, + None, + data["params"], + tag="", + log=False, + logspline=False, + # kind="linear", + truth=None, + dolevels=True, + latexnames=latexnames, + ) + + ########### F data for fixed values of other parameters ########### + # extracts best-fit values + list1 = [] + vals1 = [] + list2 = [] + vals2 = [] + vals3 = [] + for i, vec in enumerate(uw_vectors): + n = np.argmax(vec) # selects the most likely value + val = uvals[i][n] + if data["params"][i] == "logF": + # enables us to select a slice corresponding to particular F values + list1.append(data["params"][i]) + vals1.append(F_0) + iF = i # setting index for F param + else: + # enables us to select a slice correspondng to the best-fit values of all other params + # i.e. ignoring uncertainty in them + list2.append(data["params"][i]) + vals2.append(val) + + # gets the slice corresponding to specific values of F + F_0_selection = ac.get_slice_from_parameters(data, list1, vals1, verbose=True) + + # will have Bayesian limits on all parameters over everything but F + deprecated, F_vectors, deprecated = ac.get_bayesian_data(F_0_selection) + + ####### 1D plots for prior on F ######## + # generates plots for our standard prior on F only + # applies a prior on F, which is a Gaussian + F_dim = np.where(data["params"] == "logF")[0][0] + + wlls = ac.apply_F_prior(data["ll"], F_dim, data["logF"], F_0, F_sigma) + + deprecated, wF_vectors, wvectors = ac.get_bayesian_data(wlls) + + ac.do_single_plots( + uvals, + wF_vectors, + None, + data["params"], + tag="wF_", + truth=None, + dolevels=True, + latexnames=latexnames, + logspline=False, + ) + + # now do this with others... + # builds others... + others = [] + for i, p in enumerate(data["params"]): + if i == iF: + oset = None + others.append(oset) + else: + if i < iF: + modi = i + else: + modi = i - 1 + oset = [uw_vectors[i]] + others.append(oset) + + # generates plots for our standard prior on F values, and no prior also + ac.do_single_plots( + uvals, + wF_vectors, + None, + data["params"], + tag="wF_others_", + truth=None, + dolevels=True, + latexnames=latexnames, + logspline=False, + others=others, + ) + + +def get_names_values(data): + """ + Gets a list of latex names and corrected parameter values + """ + # builds uvals list + uvals = [] + latexnames = [] + for ip, param in enumerate(data["params"]): + # switches for alpha + if param == "alpha": + uvals.append(data[param] * -1.0) + else: + uvals.append(data[param]) + if param == "alpha": + latexnames.append("$\\alpha$") + ialpha = ip + elif param == "lEmax": + latexnames.append("$\\log_{10} E_{\\rm max}$") + elif param == "H0": + latexnames.append("$H_0$") + elif param == "gamma": + latexnames.append("$\\gamma$") + elif param == "sfr_n": + latexnames.append("$n_{\\rm sfr}$") + elif param == "lmean": + latexnames.append("$\\mu_{\\rm host}$") + elif param == "lsigma": + latexnames.append("$\\sigma_{\\rm host}$") + elif param == "logF": + latexnames.append("$\\log F$") + return uvals, latexnames + + +def get_param_values(data, verbose=False): + """ + Returns the unique cube values for each parameter in the cube + + Input: + data cube (tuple from reading the .npz) + + Output: + list of numpy arrays for each parameter giving their values + """ + # gets unique values for each axis + param_vals = [] + + # for col in param_list: + for col in data["params"]: + # unique=np.unique(col) + unique = np.unique(data[col]) + param_vals.append(unique) + if verbose: + print("For parameter ", col, " cube values are ", unique) + return param_vals + + +main() diff --git a/zdm/analyze_cube.py b/zdm/analyze_cube.py index 195eeb46..228c24dc 100644 --- a/zdm/analyze_cube.py +++ b/zdm/analyze_cube.py @@ -253,6 +253,57 @@ def apply_H0_prior( return wlls +def apply_F_prior( + lls: np.ndarray, + Fdim: int, + Fvalues: np.ndarray, + F_0: float, + F_sigma: float, +): + """ + Applies a prior as a function of F + + This is a Gaussian prior. + + Args: + lls (np.ndarray): values of likelihoods + + Fdim (int): dimension of F0 in the data + + Fvalues (float): vector specifying values of H0 + + F_0 (float): value of F + + F_sigma (float): 1 sigma uncertainty on F + + Returns a vector of length lls modified + by that prior. + """ + + NDIMS = len(lls.shape) + if Fdim < 0 or Fdim >= NDIMS: + raise ValueError( + "Data only has ", + NDIMS, + " dimensions.", + "Please select F dim between 0 and ", + NDIMS - 1, + " not ", + Fdim, + ) + + wlls = np.copy(lls) + + for iv, val in enumerate(Fvalues): + # select ivth value from iparam dimension + big_slice = [slice(None, None, None)] * NDIMS + big_slice[Fdim] = iv + weight = -0.5 * ((val - F_0) / F_sigma) ** 2 * np.log10(np.exp(1)) + wlls[tuple(big_slice)] += weight + + return wlls + + def get_slice_from_parameters(data, plist, mcvals, verbose=False, wanted="ll"): """ Selects from data according to parameters which are @@ -379,8 +430,9 @@ def get_bayesian_data(lls: np.ndarray, plls: np.ndarray = None, pklfile=None): lls = origlls[tuple(big_slice)].flatten() # ignores all values of 0, which is what missing data is - ignore = np.where(lls == 0.0)[0] - lls[ignore] = -99999 + # ignore = np.where(lls == 0.0)[0] + # lls[ignore] = -99999 + lls[np.isnan(lls)] = -99999 # selects all fits that are close to the peak (i.e. percentage within 0.1%) try: @@ -1182,6 +1234,8 @@ def do_single_plots(uvals,vectors,wvectors,names,tag=None, fig_exten='.png', plt.plot( x, y, color="grey", linewidth=1, linestyle=other_styles[io % 3] ) + + plt.legend() if dolevels: string += "\\\\" if log: diff --git a/zdm/scripts/plot_limits_from_cube.py b/zdm/scripts/plot_limits_from_cube.py index f90af8ad..6fed7128 100644 --- a/zdm/scripts/plot_limits_from_cube.py +++ b/zdm/scripts/plot_limits_from_cube.py @@ -23,7 +23,6 @@ def main(verbose=False): - ######### sets the values of H0 for priors ##### Planck_H0 = 67.4 Planck_sigma = 0.5 @@ -32,6 +31,7 @@ def main(verbose=False): ##### loads cube data ##### # cube = "../../papers/F/Analysis/Real/Cubes/craco_real_cube.npz" + # cube = "../../papers/F/Analysis/Real/Cubes/craco_real_old_cube.npz" cube = "../../papers/F/Analysis/CRACO/Cubes/craco_full_cube.npz" data = np.load(cube) if verbose: @@ -144,13 +144,13 @@ def main(verbose=False): latexnames=latexnames, logspline=False, others=others, + others_labels=["No Prior", r"$H_0 = 73.04$", r"$H_0 = 67.4$"], ) ############## 2D plots for total likelihood ########### # these are for nor priors on anything baduvals, ijs, arrays, warrays = ac.get_2D_bayesian_data(data["ll"]) for which, array in enumerate(arrays): - i = ijs[which][0] j = ijs[which][1] @@ -213,10 +213,10 @@ def get_names_values(data): def get_param_values(data, verbose=False): """ Returns the unique cube values for each parameter in the cube - + Input: data cube (tuple from reading the .npz) - + Output: list of numpy arrays for each parameter giving their values """ @@ -225,7 +225,6 @@ def get_param_values(data, verbose=False): # for col in param_list: for col in data["params"]: - # unique=np.unique(col) unique = np.unique(data[col]) param_vals.append(unique) From ab4f75e378b2e0143a5dfcb65e0b71d82fd0d1ec Mon Sep 17 00:00:00 2001 From: profxj Date: Wed, 10 May 2023 04:48:57 -0700 Subject: [PATCH 097/104] TNS --- zdm/data/Surveys/CRAFT_ICS.ecsv | 6 +++--- zdm/data/Surveys/CRAFT_ICS_1632.ecsv | 2 +- zdm/data/Surveys/CRAFT_ICS_892.ecsv | 6 +++--- 3 files changed, 7 insertions(+), 7 deletions(-) diff --git a/zdm/data/Surveys/CRAFT_ICS.ecsv b/zdm/data/Surveys/CRAFT_ICS.ecsv index d1604a5c..f8dd9972 100644 --- a/zdm/data/Surveys/CRAFT_ICS.ecsv +++ b/zdm/data/Surveys/CRAFT_ICS.ecsv @@ -32,6 +32,6 @@ TNS BW DM DMG FBAR FRES Gb Gl SNR SNRTHRESH THRESH TRES WIDTH XDec XRA Z 20191228A 336.0 297.5 32.9 1271.5 1.0 "" "" 22.9 9.0 4.4 1.728 2.3 -29:35:37.85 22:57:43.269 0.243 20210117A 336.0 730.0 34.4 1271.5 1.0 "" "" 27.1 9.0 4.4 1.182 3.4 -16:11:25.2 22:39:36.0 0.214 20210214A 336.0 398.3 31.9 1271.5 1.0 "" "" 11.6 9.0 4.4 1.182 3.5 -05:49:56 00:27:43 -1.0 -20210407 336.0 1785.3 154.0 1271.5 1.0 "" "" 19.1 9.0 4.4 1.182 8.0 27:03:30.24 05:14:36.202 -1.0 -20210912 336.0 1234.5 30.9 1271.5 1.0 "" "" 31.7 9.0 4.4 1.182 5.5 -30:29:33.1 23:24:40.3 -1.0 -20211127 336.0 234.83 42.5 1271.5 1.0 "" "" 37.9 9.0 4.4 1.182 1.41 -18:49:28.4 13:19:09.5 0.046946 +20210407E 336.0 1785.3 154.0 1271.5 1.0 "" "" 19.1 9.0 4.4 1.182 8.0 27:03:30.24 05:14:36.202 -1.0 +20210912A 336.0 1234.5 30.9 1271.5 1.0 "" "" 31.7 9.0 4.4 1.182 5.5 -30:29:33.1 23:24:40.3 -1.0 +20211127I 336.0 234.83 42.5 1271.5 1.0 "" "" 37.9 9.0 4.4 1.182 1.41 -18:49:28.4 13:19:09.5 0.046946 diff --git a/zdm/data/Surveys/CRAFT_ICS_1632.ecsv b/zdm/data/Surveys/CRAFT_ICS_1632.ecsv index c4f6f1c9..9284dcec 100644 --- a/zdm/data/Surveys/CRAFT_ICS_1632.ecsv +++ b/zdm/data/Surveys/CRAFT_ICS_1632.ecsv @@ -23,4 +23,4 @@ # BEAM\": \"lat50_log\",\n \"DIAM\": 12.0,\n \"NBEAMS\": 1\n }\n}"} # schema: astropy-2.0 TNS BW DM DMG FBAR FRES Gb Gl SNR SNRTHRESH THRESH TRES WIDTH XC XDec XRA Z -20211212 336.0 206.0 27.1 1632.5 1.0 "" "" 12.8 9.0 4.4 1.182 2.7 closepack36/45/0.9 01:40:36.8 10:30:40.7 0.0715 +20211212A 336.0 206.0 27.1 1632.5 1.0 "" "" 12.8 9.0 4.4 1.182 2.7 closepack36/45/0.9 01:40:36.8 10:30:40.7 0.0715 diff --git a/zdm/data/Surveys/CRAFT_ICS_892.ecsv b/zdm/data/Surveys/CRAFT_ICS_892.ecsv index b682ff6b..a58bfa17 100644 --- a/zdm/data/Surveys/CRAFT_ICS_892.ecsv +++ b/zdm/data/Surveys/CRAFT_ICS_892.ecsv @@ -27,7 +27,7 @@ TNS BW DM DMG FBAR FRES Gb Gl SNR SNRTHRESH THRESH TRES WIDTH XC XDec XRA Z 20200430A 336.0 380.1 27.0 864.5 1.0 "" "" 16.0 9.0 4.4 1.728 6.5 square_6x6 12:22:34.007 15:18:49.581 0.161 20200627 336.0 294.0 40.0 920.5 1.0 "" "" 11.0 9.0 4.4 1.728 2.9 closepack36 -39:29:05.0 21:46:47.0 -1.0 20200906A 336.0 577.8 35.9 864.5 1.0 "" "" 19.2 9.0 4.4 1.728 6.0 closepack36 -14:04:59.9136 03:33:58.9364 0.36879 -20210320 336.0 384.8 42.2 864.5 1.0 "" "" 15.3 9.0 4.4 1.728 5.4 square_6x6/45/1.05 -16:09:05.1 13:37:50.08605 0.28 -20210807 336.0 251.9 121.2 920.5 1.0 "" "" 47.1 9.0 4.4 1.182 10.0 square6x6/45/0.9 -00:45:44.5 19:56:53.144 0.12969 +20210320C 336.0 384.8 42.2 864.5 1.0 "" "" 15.3 9.0 4.4 1.728 5.4 square_6x6/45/1.05 -16:09:05.1 13:37:50.08605 0.28 +20210807D 336.0 251.9 121.2 920.5 1.0 "" "" 47.1 9.0 4.4 1.182 10.0 square6x6/45/0.9 -00:45:44.5 19:56:53.144 0.12969 20210809 336.0 651.5 190.1 920.5 1.0 "" "" 16.8 9.0 4.4 1.182 14.2 square6x6/45/0.9 01:19:43.5 18:04:37.7 -1.0 -20211203 336.0 636.2 63.4 920.5 1.0 "" "" 14.2 9.0 4.4 1.182 9.6 closepack36/45/0.9 -31:22:04.0 13:37:52.8 0.34386 +20211203C 336.0 636.2 63.4 920.5 1.0 "" "" 14.2 9.0 4.4 1.182 9.6 closepack36/45/0.9 -31:22:04.0 13:37:52.8 0.34386 From 1eba11c977ec7ac609e25af833b92a7d1c052eaf Mon Sep 17 00:00:00 2001 From: profxj Date: Wed, 10 May 2023 05:21:55 -0700 Subject: [PATCH 098/104] 2 more --- zdm/data/Surveys/CRAFT_ICS_892.ecsv | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/zdm/data/Surveys/CRAFT_ICS_892.ecsv b/zdm/data/Surveys/CRAFT_ICS_892.ecsv index a58bfa17..26266063 100644 --- a/zdm/data/Surveys/CRAFT_ICS_892.ecsv +++ b/zdm/data/Surveys/CRAFT_ICS_892.ecsv @@ -25,9 +25,9 @@ TNS BW DM DMG FBAR FRES Gb Gl SNR SNRTHRESH THRESH TRES WIDTH XC XDec XRA Z 20191001A 336.0 506.92 44.2 919.5 1.0 "" "" 62.0 9.0 4.4 1.728 4.2 closepack36/45 -54:44:54 21:33:24 0.23 20200430A 336.0 380.1 27.0 864.5 1.0 "" "" 16.0 9.0 4.4 1.728 6.5 square_6x6 12:22:34.007 15:18:49.581 0.161 -20200627 336.0 294.0 40.0 920.5 1.0 "" "" 11.0 9.0 4.4 1.728 2.9 closepack36 -39:29:05.0 21:46:47.0 -1.0 +20200627A 336.0 294.0 40.0 920.5 1.0 "" "" 11.0 9.0 4.4 1.728 2.9 closepack36 -39:29:05.0 21:46:47.0 -1.0 20200906A 336.0 577.8 35.9 864.5 1.0 "" "" 19.2 9.0 4.4 1.728 6.0 closepack36 -14:04:59.9136 03:33:58.9364 0.36879 20210320C 336.0 384.8 42.2 864.5 1.0 "" "" 15.3 9.0 4.4 1.728 5.4 square_6x6/45/1.05 -16:09:05.1 13:37:50.08605 0.28 20210807D 336.0 251.9 121.2 920.5 1.0 "" "" 47.1 9.0 4.4 1.182 10.0 square6x6/45/0.9 -00:45:44.5 19:56:53.144 0.12969 -20210809 336.0 651.5 190.1 920.5 1.0 "" "" 16.8 9.0 4.4 1.182 14.2 square6x6/45/0.9 01:19:43.5 18:04:37.7 -1.0 +20210809C 336.0 651.5 190.1 920.5 1.0 "" "" 16.8 9.0 4.4 1.182 14.2 square6x6/45/0.9 01:19:43.5 18:04:37.7 -1.0 20211203C 336.0 636.2 63.4 920.5 1.0 "" "" 14.2 9.0 4.4 1.182 9.6 closepack36/45/0.9 -31:22:04.0 13:37:52.8 0.34386 From 9273f9416ce28a9b9ca7ed986041229bb33526bb Mon Sep 17 00:00:00 2001 From: Jay Baptista Date: Mon, 29 May 2023 18:11:27 -1000 Subject: [PATCH 099/104] make tables comp. generatable --- papers/F/Analysis/CRACO/2d.ipynb | 241 --- papers/F/Analysis/CRACO/Fussing_on_F_H0.ipynb | 1433 ----------------- .../F/Analysis/CRACO/Fussing_on_F_lmean.ipynb | 269 ---- papers/F/Analysis/CRACO/Fussing_on_Full.ipynb | 1316 --------------- papers/F/Analysis/CRACO/make_fig10.py | 59 - .../CRACO/{testF.py => make_ll_2D_F.py} | 0 .../CRACO/{test.py => make_ll_2D_H0.py} | 0 papers/F/Analysis/CRACO/marginalize.ipynb | 141 -- .../F/Analysis/CRACO/py/craco_qck_explore.py | 69 +- papers/F/Analysis/Real/cube_diag.ipynb | 1377 ---------------- .../Analysis/Real/logF_host_comparison.ipynb | 132 -- .../Real/logF_sigma_comparison copy.ipynb | 109 -- .../Analysis/Real/logF_sigma_comparison.ipynb | 132 -- papers/F/Analysis/Real/make_fig10.py | 59 - papers/F/Analysis/Real/make_fig6.py | 222 --- papers/F/Analysis/Real/make_fig9.py | 156 -- papers/F/Analysis/Real/make_ll_2D_F.py | 150 ++ .../Real/{test.py => make_ll_2D_H0.py} | 0 ...ake_fig7.py => make_survey_contrib_fig.py} | 105 +- .../F/Analysis/Real/py/craco_qck_explore.py | 54 +- papers/F/Analysis/Real/testF.py | 138 -- papers/F/Analysis/Real/testing_bayesian.ipynb | 186 --- papers/F/Analysis/py/plotF_wH0Prior.py | 238 +++ ...its_from_cube.py => plotHubble_wFPrior.py} | 16 +- papers/F/Figures/py/figs_compare.py | 54 +- papers/F/Figures/py/figs_zdm_F_I.py | 42 +- papers/F/Tables/results.tex | 12 + papers/F/Tables/tab_frbs.tex | 25 + papers/F/Tables/tab_model_params.tex | 22 + papers/F/Tables/tables_F.py | 318 ++++ zdm/analyze_cube.py | 45 +- zdm/data/Surveys/test.ipynb | 199 +++ zdm/parameters.py | 359 +++-- zdm/scripts/plot_limits_from_cube.py | 1 - 34 files changed, 1452 insertions(+), 6227 deletions(-) delete mode 100644 papers/F/Analysis/CRACO/2d.ipynb delete mode 100644 papers/F/Analysis/CRACO/Fussing_on_F_H0.ipynb delete mode 100644 papers/F/Analysis/CRACO/Fussing_on_F_lmean.ipynb delete mode 100644 papers/F/Analysis/CRACO/Fussing_on_Full.ipynb delete mode 100644 papers/F/Analysis/CRACO/make_fig10.py rename papers/F/Analysis/CRACO/{testF.py => make_ll_2D_F.py} (100%) rename papers/F/Analysis/CRACO/{test.py => make_ll_2D_H0.py} (100%) delete mode 100644 papers/F/Analysis/CRACO/marginalize.ipynb delete mode 100644 papers/F/Analysis/Real/cube_diag.ipynb delete mode 100644 papers/F/Analysis/Real/logF_host_comparison.ipynb delete mode 100644 papers/F/Analysis/Real/logF_sigma_comparison copy.ipynb delete mode 100644 papers/F/Analysis/Real/logF_sigma_comparison.ipynb delete mode 100644 papers/F/Analysis/Real/make_fig10.py delete mode 100644 papers/F/Analysis/Real/make_fig6.py delete mode 100644 papers/F/Analysis/Real/make_fig9.py create mode 100644 papers/F/Analysis/Real/make_ll_2D_F.py rename papers/F/Analysis/Real/{test.py => make_ll_2D_H0.py} (100%) rename papers/F/Analysis/Real/{make_fig7.py => make_survey_contrib_fig.py} (71%) delete mode 100644 papers/F/Analysis/Real/testF.py delete mode 100644 papers/F/Analysis/Real/testing_bayesian.ipynb create mode 100644 papers/F/Analysis/py/plotF_wH0Prior.py rename papers/F/Analysis/py/{plot_limits_from_cube.py => plotHubble_wFPrior.py} (93%) create mode 100644 papers/F/Tables/results.tex create mode 100644 papers/F/Tables/tab_frbs.tex create mode 100644 papers/F/Tables/tab_model_params.tex create mode 100644 papers/F/Tables/tables_F.py create mode 100644 zdm/data/Surveys/test.ipynb diff --git a/papers/F/Analysis/CRACO/2d.ipynb b/papers/F/Analysis/CRACO/2d.ipynb deleted file mode 100644 index d4879c02..00000000 --- a/papers/F/Analysis/CRACO/2d.ipynb +++ /dev/null @@ -1,241 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "caf56b85-94a0-4e98-8c2a-7dba1111aa23", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import zdm.analyze_cube as ac\n", - "cube_dir = \"../CRACO/Cubes/craco_mini_cube.npz\"\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "f5e56bdd-a957-4150-a519-5dbedeeece6b", - "metadata": {}, - "outputs": [], - "source": [ - "cube=np.load(cube_dir)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "8527cf7d-c26f-47c5-a71c-1aee29ca3f6e", - "metadata": {}, - "outputs": [], - "source": [ - "lls = cube[\"ll\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "7514ab70-4500-420e-b578-30cad4d806f9", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jovyan/zdm/zdm/analyze_cube.py:505: RuntimeWarning: All-NaN slice encountered\n", - " themax = np.nanmax(lls)\n", - "/home/jovyan/zdm/zdm/analyze_cube.py:517: RuntimeWarning: All-NaN slice encountered\n", - " wthemax = np.nanmax(wlls)\n" - ] - } - ], - "source": [ - "uvals, ijs, arrays, warrays = ac.get_2D_bayesian_data(cube['ll'])" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "61316a13-7fc2-47ee-85ca-549bba5774f0", - "metadata": {}, - "outputs": [], - "source": [ - "def ij_idx(param_1, param_2):\n", - " idx_1 = np.where(cube[\"params\"] == param_1)[0][0]\n", - " idx_2 = np.where(cube[\"params\"] == param_2)[0][0]\n", - " return np.where((np.array(ijs) == [idx_1, idx_2])[:, 0] & (np.array(ijs) == [idx_1, idx_2])[:, 1])[0][0]" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "294f18fb-3a00-40a0-b7dd-cf61500c7729", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(dpi=100)\n", - "\n", - "xx, yy = np.meshgrid(cube[\"H0\"], 10**(cube[\"logF\"]))\n", - "data = np.array(arrays[ij_idx(\"H0\", \"logF\")])\n", - "\n", - "array = data\n", - "# array -= np.max(array)\n", - "# array = 10**array\n", - "# array /= np.sum(array)\n", - "\n", - "f = ax.pcolormesh(xx, yy, array.T)\n", - "ax.set_xlabel(r\"$H_0$\")\n", - "ax.set_ylabel(\"F\")\n", - "plt.colorbar(f, ax=ax, label=r\"$p(H_0 | F)$\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "6e84f3ce-e426-4104-8440-ed40325ad205", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(dpi=100)\n", - "\n", - "xx, yy = np.meshgrid(cube[\"H0\"], (cube[\"logF\"]))\n", - "data = np.array(arrays[ij_idx(\"H0\", \"logF\")])\n", - "\n", - "array = data\n", - "# array -= np.max(array)\n", - "# array = 10**array\n", - "# array /= np.sum(array)\n", - "\n", - "f = ax.pcolormesh(xx, yy, array.T)\n", - "ax.set_xlabel(r\"$H_0$\")\n", - "ax.set_ylabel(r\"$\\log F$\")\n", - "plt.colorbar(f, ax=ax, label=r\"$p(H_0 | \\log F)$\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "1aca9fda-7e1f-4e14-9d25-403147e697c0", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(dpi=100)\n", - "\n", - "xx, yy = np.meshgrid(cube[\"lmean\"], 10**(cube[\"logF\"]))\n", - "data = np.array(arrays[ij_idx(\"lmean\", \"logF\")])\n", - "ax.pcolormesh(xx, yy, data.T)\n", - "ax.set_xlabel(\"lmean\")\n", - "ax.set_ylabel(\"F\")\n", - "plt.colorbar(f, ax=ax, label=r\"$p(\\mathrm{lmean} | F)$\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "da910ce0-deb7-4a39-a273-08022adc146d", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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yIt6MiN9rKjszIh6PiNGI2BgRUZQfExF3FOVbI2K4i6ciSSpUNUJ5ArgEeLhN/U3AfZPKbgHWA6cWj3VF+dXAa5l5StHuhtJ7K0k6okoCJTOfysynW9VFxEXAs8COprITgMWZuSUzE7gduKiovhD4RrF9J3BuY/QiSeqenlpDiYgFwGeAz0+qOhEYa3o+VpQ16p4HyMwDwOvAUJvXXx8RIxExsnv37jK7LklzXscCJSIejIgnWjwunKLZ54GbMvPNyS/XYt+cRt1bCzM3ZWYtM2tLliw58klIkqZtyq8Afjsy87wZNPsQcGlEfBF4N1CPiHHg28CKpv1WAC8U22PAScBYRMwDjgNenWm/JUkz07FAmYnMXNvYjojPAW9m5peL53sjYg2wFbgS+FKx693AVcAW4FLgoWKdRZLURVVdNnxxRIwBZwP3RMQD02h2DXArMAo8wz9cBXYbMBQRo8AG4LMd6LIk6Qhirn6Yr9VqOTIyUnU3JKmvRMS2zDzs7wehx67ykiT1LwNFklQKA0WSVAoDRZJUCgNFklQKA0WSVAoDRZJUCgNFklQKA0WSVAoDRZJUCgNFklQKA0WSVAoDRZJUCgNFklQKA0WSVAoDRZJUCgNFklQKA0WSVAoDRZJUCgNFklQKA0WSVIpKAiUiLouIHRFRj4japLoPRMSWov7xiBgsys8sno9GxMaIiKL8mIi4oyjfGhHDFZySJM15VY1QngAuAR5uLoyIecA3gU9m5mrgw8D+ovoWYD1wavFYV5RfDbyWmacANwE3dLrzkqTDVRIomflUZj7douojwA8y8/vFfnsy82BEnAAszswtmZnA7cBFRZsLgW8U23cC5zZGL5Kk7um1NZT3ARkRD0TE9yLi3xTlJwJjTfuNFWWNuucBMvMA8Dow1OrFI2J9RIxExMju3bs7cgKSNFfN69QLR8SDwPIWVddn5l1T9OeXgA8Cfw/8j4jYBrzRYt9sHGqKurcWZm4CNgHUarWW+0iSZqZjgZKZ582g2Rjw15n5CkBE3AucwcS6yoqm/VYALzS1OQkYK9ZgjgNenWm/JUkz02tTXg8AH4iIdxXh8MvAk5n5IrA3ItYU6yNXAo1Rzt3AVcX2pcBDxTqLJKmLqrps+OKIGAPOBu6JiAcAMvM14Ebgu8B24HuZeU/R7BrgVmAUeAa4ryi/DRiKiFFgA/DZbp2HJOkfxFz9MF+r1XJkZKTqbkhSX4mIbZlZa1XXa1NekqQ+ZaBIkkphoEiSSmGgSJJKYaBIkkphoEiSSmGgSJJKYaBIkkphoEiSSmGgSJJKYaBIkkphoEiSSmGgSJJKYaBIkkphoEiSSmGgSJJKYaBIkkphoEiSSmGgSJJKYaBIkkpRSaBExGURsSMi6hFRayqfHxHfiIjHI+KpiLiuqe7Monw0IjZGRBTlx0TEHUX51ogYruCUJGnOq2qE8gRwCfDwpPLLgGMy8+eBM4FPNAXELcB64NTisa4ovxp4LTNPAW4Cbuhs1yVJrVQSKJn5VGY+3aoKWBAR84BjgZ8Ab0TECcDizNySmQncDlxUtLkQ+EaxfSdwbmP0Iknqnl5bQ7kT2Ae8CPwI+KPMfBU4ERhr2m+sKKP47/MAmXkAeB0YavXiEbE+IkYiYmT37t2dOQNJmqPmdeqFI+JBYHmLqusz8642zc4CDgI/BbwHeKR4nVYjjmwcaoq6txZmbgI2AdRqtZb7SJJmpmOBkpnnzaDZbwH3Z+Z+YFdE/A1QAx4BVjTttwJ4odgeA04CxoqpsuOAV2fccUnSjPTalNePgHNiwgJgDfC/M/NFYG9ErCnWR64EGqOcu4Griu1LgYeKdRZJUhdVddnwxRExBpwN3BMRDxRV/xlYyMRVYN8F/iQzf1DUXQPcCowCzwD3FeW3AUMRMQpsAD7bnbOQJDWLufphvlar5cjISNXdkKS+EhHbMrPWqq7XprwkSX3KQJEklcJAkSSVwkCRJJXCQJEklcJAkSSVwkCRJJWiY7demY3q9WTnnn28/MY4yxYPMjy0gIEBb2wsSWCgTFu9nty/4yU2bN7O+P46g/MHuPHy01m3ermhIkk45TVtO/fsOxQmAOP762zYvJ2de/ZV3DNJ6g0GyjS9/Mb4oTBpGN9fZ9fe8Yp6JEm9xUCZpmWLBxmc/9Yf1+D8AZYuGqyoR5LUWwyUaRoeWsCNl59+KFQaayjDQwsq7pkk9QYX5adpYCBYt3o5p127ll17x1m6yKu8JKmZgXIUBgaCVUsWsmrJwqq7Ikk9xykvSVIpDBRJUikMFElSKQwUSVIpDBRJUikiM6vuQyUiYjfww6r7UZHjgVeq7kSFPP+5ff7gz+DtnP8/yswlrSrmbKDMZRExkpm1qvtRFc9/bp8/+DPo1Pk75SVJKoWBIkkqhYEyN22qugMV8/w1138GHTl/11AkSaVwhCJJKoWBIkkqhYEyi0XEuoh4OiJGI+KzLeo/FhE/KB5/GxG/UEU/O+VI59+03wcj4mBEXNrN/nXadM4/Ij4cEdsjYkdE/HW3+9hJ0/j//7iI+K8R8f3i/D9eRT87JSK+FhG7IuKJNvURERuLn88PIuKMt33QzPQxCx/AO4BngFXAO4HvA++ftM8vAu8ptn8V2Fp1v7t5/k37PQTcC1xadb+7/O//buBJYGXxfGnV/e7y+f9b4IZiewnwKvDOqvte4s/gHwNnAE+0qb8AuA8IYE0Zv/+OUGavs4DRzHw2M38CfAu4sHmHzPzbzHytePoosKLLfeykI55/4XeAbwO7utm5LpjO+f8W8J3M/BFAZs6mn8F0zj+BRRERwEImAuVAd7vZOZn5MBPn1M6FwO054VHg3RFxwts5poEye50IPN/0fKwoa+dqJj6tzBZHPP+IOBG4GPhKF/vVLdP5938f8J6I+KuI2BYRV3atd503nfP/MvCzwAvA48CnM7Pene71hKN9jzgiv7Fx9mr13cQtrxGPiF9hIlB+qaM96q7pnP8fA5/JzIMTH1Jnlemc/zzgTOBc4FhgS0Q8mpn/p9Od64LpnP/5wHbgHOCngf8eEY9k5hsd7luvmPZ7xHQZKLPXGHBS0/MVTHwSe4uI+ABwK/CrmbmnS33rhumcfw34VhEmxwMXRMSBzPzLrvSws6Zz/mPAK5m5D9gXEQ8DvwDMhkCZzvl/HPjDnFhQGI2I54DTgMe608XKTes94mg45TV7fRc4NSJOjoh3AlcAdzfvEBErge8A/3yWfCptdsTzz8yTM3M4M4eBO4F/NUvCBKZx/sBdwNqImBcR7wI+BDzV5X52ynTO/0dMjM6IiGXAzwDPdrWX1bobuLK42msN8Hpmvvh2XtARyiyVmQci4reBB5i44uVrmbkjIj5Z1H8F+H1gCLi5+JR+IGfJHVinef6z1nTOPzOfioj7gR8AdeDWzGx5iWm/mea//38Avh4RjzMx/fOZzJw1t7SPiD8HPgwcHxFjwL8H5sOh87+XiSu9RoG/Z2LE9vaOWVw+JknS2+KUlySpFAaKJKkUBookqRQGiiSpFAaKJKkUXjYs9ZCIOMjEbUAaLsrMnRV1RzoqXjYs9ZCIeDMzF1bdD2kmnPKSJJXCEYrUQyZNeT2XmRdX2R/paBgoUg9xykv9zCkvSVIpDBRJUikMFElSKVxDkSSVwhGKJKkUBookqRQGiiSpFAaKJKkUBookqRQGiiSpFAaKJKkU/x8ScrvNcLInWgAAAABJRU5ErkJggg==", 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "ax = sns.scatterplot(data=df_comb, x='F', y='lls', hue='H0')\n", - "\n", - "xlim = [0.1, 1.]\n", - "ax.set_xlim(xlim)\n", - "ax.set_ylim(-600., -550.)\n", - "\n", - "# Max line\n", - "max_LL = df_comb.lls.max()\n", - "ax.plot(xlim, [max_LL]*2, 'g--')" - ] - }, - { - "cell_type": "markdown", - "id": "b5df69af-6901-40c2-beba-0212182bdd16", - "metadata": {}, - "source": [ - "# I am suspecting a slurp bug.." - ] - }, - { - "cell_type": "markdown", - "id": "f5438dc7-61d6-4a67-9aaa-3248fbdc4a5b", - "metadata": {}, - "source": [ - "# I slurped, now am examining the slurped file" - ] - }, - { - "cell_type": "markdown", - "id": "8ef3b730-fea3-40aa-9b7d-34814e9c2024", - "metadata": {}, - "source": [ - "## Load" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "654b497a-e590-4762-a0d2-4ce1c84306dc", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['ll',\n", - " 'lC',\n", - " 'params',\n", - " 'pzDM',\n", - " 'pDM',\n", - " 'pDMz',\n", - " 'pz',\n", - " 'F',\n", - " 'H0',\n", - " 'lls0',\n", - " 'P_zDM0',\n", - " 'P_n0',\n", - " 'P_s0',\n", - " 'N0']" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cube = np.load('Cubes/craco_H0_F_cube.npz')\n", - "list(cube.keys())" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "5a1f38c3-2276-4db4-8b85-b562c218f260", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(50, 50)" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "LL = cube['ll']\n", - "LL.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "ffb3969c-843c-4186-8e8b-71132b959441", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "-567.777" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.nanmax(LL[:,0])" - ] - }, - { - "cell_type": "markdown", - "id": "77e3c617-d266-4a7f-940f-170549619732", - "metadata": {}, - "source": [ - "## Parse" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "id": "b7267261-fb2f-4686-9521-559730c3b3ed", - "metadata": {}, - "outputs": [], - "source": [ - "F = cube['F']\n", - "H0 = cube['H0']\n", - "#\n", - "dF = F[1]-F[0]\n", - "dH = H0[1] - H0[0]" - ] - }, - { - "cell_type": "markdown", - "id": "59934a22-f603-4c5a-b5b1-86b4cf7b0ac8", - "metadata": {}, - "source": [ - "## Plot" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "adf1fdda-aa1c-4ee2-850d-82f36e5835c3", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.clf()\n", - "ax = plt.gca()\n", - "ax.plot(LL[:,0])\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "9b85c517-7fcc-408c-bc68-8f85639b5201", - "metadata": {}, - "source": [ - "## Show it all" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "id": "24b700b6-1433-4a73-bde8-b5f9f9b5fd9c", - "metadata": {}, - "outputs": [], - "source": [ - "nans = np.isnan(LL)\n", - "LL_clean = LL.copy()\n", - "LL_clean[nans] = -9e9\n", - "#\n", - "LL_clean -= LL_clean.max()" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "id": "493e0416-168e-438a-9d29-40b095dbccbf", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'H0 (km/s/Mpc)')" - ] - }, - "execution_count": 59, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.clf()\n", - "ax=plt.gca()\n", - "#\n", - "im = plt.imshow(LL_clean.T, origin='lower', vmin=-4., vmax=0., cmap='jet',\n", - " extent=[F.min()-dF/2, F.max()+dF/2, 55.-dH/2, 80+dH/2], aspect='auto')\n", - "# Color bar\n", - "cbar=plt.colorbar(im,fraction=0.046, shrink=1.2,aspect=15,pad=0.05)\n", - "cbar.set_label(r'$\\Delta$ Log10 Likelihood')\n", - "#\n", - "ax.set_xlabel('F')\n", - "ax.set_ylabel('H0 (km/s/Mpc)')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1e57d2a5-f711-4e40-bcf0-4de0576ec60a", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.8.5 ('base')", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5" - }, - "vscode": { - "interpreter": { - "hash": "9731a3b7ebb41545a410367c249afbc4842fd5740da82f1bd29e6ac1d7253e6e" - } - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/papers/F/Analysis/CRACO/Fussing_on_F_lmean.ipynb b/papers/F/Analysis/CRACO/Fussing_on_F_lmean.ipynb deleted file mode 100644 index 51bc6bb9..00000000 --- a/papers/F/Analysis/CRACO/Fussing_on_F_lmean.ipynb +++ /dev/null @@ -1,269 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "8e9d01fb-000b-4558-80e8-27688eafa19e", - "metadata": {}, - "source": [ - "# Quick check" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "7a372b56-1bb5-40be-bdf8-4129f926399e", - "metadata": {}, - "outputs": [], - "source": [ - "# imports\n", - "import numpy as np\n", - "import pandas\n", - "\n", - "import seaborn as sns\n", - "\n", - "from matplotlib import pyplot as plt" - ] - }, - { - "cell_type": "markdown", - "id": "8ef3b730-fea3-40aa-9b7d-34814e9c2024", - "metadata": {}, - "source": [ - "## Load" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "654b497a-e590-4762-a0d2-4ce1c84306dc", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['ll',\n", - " 'lC',\n", - " 'params',\n", - " 'pzDM',\n", - " 'pDM',\n", - " 'pDMz',\n", - " 'pz',\n", - " 'F',\n", - " 'lmean',\n", - " 'lls0',\n", - " 'P_zDM0',\n", - " 'P_n0',\n", - " 'P_s0',\n", - " 'N0']" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cube = np.load('Cubes/craco_lm_F_cube.npz')\n", - "list(cube.keys())" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "5a1f38c3-2276-4db4-8b85-b562c218f260", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(50, 50)" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "LL = cube['ll']\n", - "LL.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "ffb3969c-843c-4186-8e8b-71132b959441", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "-573.6371" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.nanmax(LL[:,0])" - ] - }, - { - "cell_type": "markdown", - "id": "77e3c617-d266-4a7f-940f-170549619732", - "metadata": {}, - "source": [ - "## Parse" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "b7267261-fb2f-4686-9521-559730c3b3ed", - "metadata": {}, - "outputs": [], - "source": [ - "F = cube['F']\n", - "lm = cube['lmean']\n", - "#\n", - "dF = F[1]-F[0]\n", - "dlm = lm[1] - lm[0]" - ] - }, - { - "cell_type": "markdown", - "id": "59934a22-f603-4c5a-b5b1-86b4cf7b0ac8", - "metadata": {}, - "source": [ - "## Plot" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "adf1fdda-aa1c-4ee2-850d-82f36e5835c3", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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xvIlWC4KIGAbOBG4GHgR2FeEBcGGx7UEz4RiDJPV18PkK4I8i4vvAC4HfzMwEbouIb0TEtbTOSnpfZj4FEBFvBj4WEVuAKvDZPtZvNxPTxXzP3hJDUon17QiYmTcCNy6w7uMLlN8NvKNfddqT2RaDYwySyssL3Lp4uqokGQw9JqfrVCvBcud7llRiHgG7TNQarFpWJSIGXRVJGhiDoct4zWk9Jclg6DI5XWelwSCp5AyGLuNO0iNJBkO31rSenqoqqdwMhi5O0iNJBkOPiWkHnyXJYOgyWWt4Z1VJpWcwdPF0VUkyGDrqjSa1etMxBkmlZzAU2ndWtStJUtkZDAUn6ZGkFoOh4CQ9ktRiMBQ6k/Q4F4OkkjMYCp0Wg7O3SSo5g6HgJD2S1GIwFCanDQZJAoOhY7zmGIMkgcHQ4emqktRiMBQma3Ui4IhhWwySys1gKIzXGqxaNuR8z5JKz2AotOZisLUgSQZDYWK67jUMkoTB0OHsbZLUYjAUJmoNu5IkCYOhw64kSWoxGAp2JUlSi8FQGK81DAZJwmDomJyus8rZ2yTJYABoNpPJaVsMkgQGAwCTM60b6HmfJEmCvh0JI+Jc4D3APcBZwB9k5tZi3QeAEeBo4CuZ+eWi/BzgSuAhYB3w/sys96uObe0b6K30dFVJ6l8wAH8B/GZmfjciXgd8CvjHEXE+cHFmviYihoB7I+KbwE7gc8ClmTkaEZ8E3gZc18c6ArOT9NhikKT+diWdBmwtlh8EXlksvxa4FaBoDdwLXAScDByRmaPFdrcAl8/3xhFxRURsjojNO3bsWHRFJ9tzMXgdgyQtLhgi4qaIuHuen9cD3wZeVmz6UmBV0UJYB4x1vc3Oomyh8t1k5sbM3JCZG9auXbuYXQBmWwx2JUnSIruSMvOyhdZFxG3AuyPiVFoH+Z9nZj0itgNrujYdAbYXP/OV952T9EjSrH52JT07M38/M/898Cjwl0X5JuACgIgYBs4EbqbV3bQrIo4vtruw2LbvJpzvWZI6+nkkfF1EtM9KOhL4KEBm3hYR34iIa2mdlfS+zHwKICLeDHwsIrYAVeCzfaxfx4RjDJLU0bcjYWZe8wzrPr5A+d3AO/pVp4W0u5K8u6okeYEbMNuVtNIWgyQZDNBqMRwxXKVacb5nSTIY8M6qktTNYKC4s6rjC5IEGAxAMUmP4wuSBBgMQOvKZy9uk6QWgwGYnG54OwxJKhgMtFoMDj5LUovBQGuMYbVjDJIEGAxA67bbdiVJUkvpgyEzmZh28FmS2kofDLtmGjTTO6tKUlvpg2H2zqp2JUkSGAxdd1a1xSBJYDA4SY8kzWEwOEmPJPUwGJykR5J6GAxFV5Knq0pSi8FQtBhWGgySBBgMjBdjDN4SQ5JaSh8Mk50Wg2MMkgQGA+PTdZYNVRiulv5XIUmAwdC6s6rjC5LUUfpgmKw1PFVVkrqUPhjGne9ZknqUPhgmpp29TZK6GQy1hsEgSV0MhlrdW25LUheDoWZXkiR1MximG56uKkldSh0MmclErc5Ku5IkqaPUwVCrN6k3064kSepS6mCYnC5uoGcwSFLHoo6IEVEBfgv4N8ArM/OHXeveDJwLNICfZuZ/LspPAv4A+AlwEvC+zBwv3utaYKwovy4zb1tM/fakc8ttu5IkqWOxfyqfDdwOTHYXRsTzgPcD52ZmRsR3IuL/ZOaPgU8DV2fmHRHxLuCDtILiTcBIZl4VEccAt0XEmZnZWGQdFzRec5IeSZprUV1JmfndzLx7nlWXAXdmZhbPbwVeHRHDwMXAd4ryW4DLi+XLi+3IzCeBKeCs+T43Iq6IiM0RsXnHjh37Xf/JaSfpkaS59nhEjIibgOPmWXV1Zn55gZeto9Ul1LazKDsW2NUVGO3yZ3rNbjJzI7ARYMOGDTnfNnujM0mPN9GTpI49BkNmXrYf77sdOLXr+QitMYXHgSMiIopwGCm2bb9mzZzXbKeP2pP0eFaSJM3q11lJNwHnRUQUzy8A/ndmzgDfAF5SlF8IbCqWNxXbUYwxrAB+1Kf6AbNjDN5dVZJmLfaspKOBK4EjgSsi4q8y87bMfCQiPgH8u4hoAP+1GHgG+G3g6oj4ZWA98HtF+Q3AuRHxh0X5W/s58AyzZyXZYpCkWYs6ImbmL4Brip+56z4HfG6e8p8Bb5+nvEnrDKWDZqK4jsGJeiRpVqkvcJuo1RmqBMuc71mSOkp9RGzfWXV2KESSVO5g8M6qkrSbcgeDd1aVpN2UOhjGnaRHknZT6mCYtCtJknZT6mCwK0mSdlfqYBiv1W0xSNIcpQ6GyemGYwySNEepg2G8VmelVz1LUo/SBsNMo8l0vclqb6AnST1KGwyTtfZ9kgwGSepW2mAYn27fWdWuJEnqVtpg8JbbkjQ/g8FgkKQeJQ6GYozBwWdJ6lHaYOhM6+kYgyT1KG0wrF2zjFf/0vEcu3r5oKsiSYeU0vajnHfiMZx34jGDroYkHXJK22KQJM3PYJAk9TAYJEk9DAZJUg+DQZLUw2CQJPUwGCRJPQwGSVKPyMxB12FRImIHsGUPmx0LPH4QqnMoKvO+Q7n3v8z7DuXe/73Z9xMzc+18Kw77YNgbEbE5MzcMuh6DUOZ9h3Lvf5n3Hcq9/4vdd7uSJEk9DAZJUo+yBMPGQVdggMq871Du/S/zvkO5939R+16KMQZJ0t4rS4tBkrSXDAZJUo8lP1FPRFwK/CqwHcjM/MiAq9Q3EXE8cA1wdma+pChbAXwCeBQ4DfjjzHxgcLXsj4g4hda+3wU8D3giMz8aEccAfww8SGv/P5SZ2wZX0/6IiArwt8DtwDLgFODtwBGUYP8BIuIIWvv/lcx8f1n+7QNExG3AVPG0kZmXLObf/pIOhohYCXwaOCszaxHxhYi4JDO/Pui69ck/Ar4EnNNV9l5ga2b+24h4EXAd8PKDX7W+Owb4fGZ+CSAi7omITcBvAV/LzBsi4nW0DhRvGWA9++nWzLwGICK+ROsPopdTnv2/Bvhu1/P3Uo5/+wB/n5kfnlN2Lfv53S/1rqQLgC2ZWSue3wJcPsD69FVm/jUwNqf4cuDWYv0PgLMjYuRg163fMvM77VAoVIAJuvafJfz9Z2azKxSGaLWa7qck+x8Rb6G1fw91FZfi337hRRHxwYj4cES0v+P9/u6XdIsBWEfvgXJnUVYmC/0Odg6mOv0XEW8AbsrM+yKie/93AkdHxFBm1gdXw/6JiMuA3wX+LjM3l2H/I+KFwJmZ+aGIeHHXqjL92/+TzLwjIqrAzRExRu/+79N3v9RbDNuBNV3PR4qyMinV7yAiLgYupnVwhN79HwF+sZQOinNl5k2Z+SvA8yPinZRj/98ATEXEVbS6U18aEe+lRP/2M/OO4rEBfIvW/4H9/u6XeovhVuDEiFhedCddCPynAdfpYNtEq0vtW0U/6/cycyn+xUTRhH458B7g2RFxIrP7/zCt73/T4GrYP8Vfzc/PzPb+PQScTAn2PzM/1l4uBpxXZ+afFstL/t9+RLwAuDAzryuKTgNuZBHf/ZK/wC0iXgW8EdgBzCzxs5IuAt4K/Arw58Ani1WfAB4DTgWuXYpnZkTEecA3gc1F0Srgz4AvA39C6w68pwBXLcWzcoqzsj5O66ysYeBM4N3ANCXYf4CI+CfAlbTOyvoz4IuU49/+c4D/SGvgfYTW9/97wFHs53e/5INBkrRvlvoYgyRpHxkMkqQeBoMkqYfBIEnqYTBIknoYDJKkHgaDJKnH/weQZS5Q4oDmOAAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.clf()\n", - "ax = plt.gca()\n", - "ax.plot(LL[:,0])\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "9b85c517-7fcc-408c-bc68-8f85639b5201", - "metadata": {}, - "source": [ - "## Show it all" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "24b700b6-1433-4a73-bde8-b5f9f9b5fd9c", - "metadata": {}, - "outputs": [], - "source": [ - "nans = np.isnan(LL)\n", - "LL_clean = LL.copy()\n", - "LL_clean[nans] = -9e9\n", - "#\n", - "LL_clean -= LL_clean.max()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "493e0416-168e-438a-9d29-40b095dbccbf", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'lmean')" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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41500465.02.0555560.2-1.73.615784-2571.122245-2432.257919-1.899126-136.9652001000.0-2571.122245-2432.257919-1.899126-136.965200-2185.932142-246.325778-2220.175469-212.082451
\n", - "
" - ], - "text/plain": [ - " n H0 lmean lsigma logF lC lls0 P_zDM0 \\\n", - "0 15000 65.0 1.700000 0.2 -1.7 3.604749 -2528.555818 -2389.721168 \n", - "1 15001 65.0 1.788889 0.2 -1.7 3.606677 -2529.497269 -2390.655305 \n", - "2 15002 65.0 1.877778 0.2 -1.7 3.609075 -2536.536738 -2397.687077 \n", - "3 15003 65.0 1.966667 0.2 -1.7 3.612061 -2550.325947 -2411.468768 \n", - "4 15004 65.0 2.055556 0.2 -1.7 3.615784 -2571.122245 -2432.257919 \n", - "\n", - " P_n0 P_s0 N0 lls P_zDM P_n \\\n", - "0 -1.899126 -136.935524 1000.0 -2528.555818 -2389.721168 -1.899126 \n", - "1 -1.899126 -136.942838 1000.0 -2529.497269 -2390.655305 -1.899126 \n", - "2 -1.899126 -136.950535 1000.0 -2536.536738 -2397.687077 -1.899126 \n", - "3 -1.899126 -136.958052 1000.0 -2550.325947 -2411.468768 -1.899126 \n", - "4 -1.899126 -136.965200 1000.0 -2571.122245 -2432.257919 -1.899126 \n", - "\n", - " P_s p_zgDM p_DM p_DMgz p_z \n", - "0 -136.935524 -2141.448538 -248.272630 -2177.664558 -212.056610 \n", - "1 -136.942838 -2143.037752 -247.617553 -2178.593204 -212.062100 \n", - "2 -136.950535 -2150.683298 -247.003779 -2185.618798 -212.068279 \n", - "3 -136.958052 -2164.946656 -246.522112 -2199.393663 -212.075105 \n", - "4 -136.965200 -2185.932142 -246.325778 -2220.175469 -212.082451 " - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_6.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "494bcf9a-bbe4-45c8-b1bc-34e2122dac9e", - "metadata": {}, - "outputs": [], - "source": [ - "idx_677 = np.isclose(df_6.H0, 65.)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "acd86e1b-528d-4462-9eb4-43d79a8167fb", - "metadata": {}, - "outputs": [], - "source": [ - "df_677 = df_6[idx_677].copy()" - ] - }, - { - "cell_type": "markdown", - "id": "3568f064-24f7-4737-8c80-fd0b10274d1e", - "metadata": {}, - "source": [ - "## Plot" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "bc61ed97-e0b2-4ddc-afc5-665bca2869f1", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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6000 rows × 19 columns

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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "ax = sns.scatterplot(data=df_comb, x='logF', y='lls', hue='H0')\n", - "\n", - "xlim = [-1, 0]\n", - "ax.set_xlim(xlim)\n", - "ax.set_ylim(-600., -550.)\n", - "\n", - "# Max line\n", - "max_LL = df_comb.lls.max()\n", - "ax.plot(xlim, [max_LL]*2, 'g--')" - ] - }, - { - "cell_type": "markdown", - "id": "b5df69af-6901-40c2-beba-0212182bdd16", - "metadata": {}, - "source": [ - "# I am suspecting a slurp bug.." - ] - }, - { - "cell_type": "markdown", - "id": "f5438dc7-61d6-4a67-9aaa-3248fbdc4a5b", - "metadata": {}, - "source": [ - "# I slurped, now am examining the slurped file" - ] - }, - { - "cell_type": "markdown", - "id": "8ef3b730-fea3-40aa-9b7d-34814e9c2024", - "metadata": {}, - "source": [ - "## Load" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "654b497a-e590-4762-a0d2-4ce1c84306dc", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['ll',\n", - " 'lC',\n", - " 'params',\n", - " 'pzDM',\n", - " 'pDM',\n", - " 'pDMz',\n", - " 'pz',\n", - " 'H0',\n", - " 'lmean',\n", - " 'lsigma',\n", - " 'logF',\n", - " 'lls0',\n", - " 'P_zDM0',\n", - " 'P_n0',\n", - " 'P_s0',\n", - " 'N0']" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cube = np.load('Cubes/craco_full_cube.npz')\n", - "list(cube.keys())" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "5a1f38c3-2276-4db4-8b85-b562c218f260", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(21, 10, 10, 30)" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "LL = cube['ll']\n", - "LL.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "ffb3969c-843c-4186-8e8b-71132b959441", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "-569.1069" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.nanmax(LL[:,0])" - ] - }, - { - "cell_type": "markdown", - "id": "77e3c617-d266-4a7f-940f-170549619732", - "metadata": {}, - "source": [ - "## Parse" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "b7267261-fb2f-4686-9521-559730c3b3ed", - "metadata": {}, - "outputs": [], - "source": [ - "F = cube['logF']\n", - "H0 = cube['H0']\n", - "#\n", - "dF = F[1]-F[0]\n", - "dH = H0[1] - H0[0]" - ] - }, - { - "cell_type": "markdown", - "id": "59934a22-f603-4c5a-b5b1-86b4cf7b0ac8", - "metadata": {}, - "source": [ - "## Plot" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "adf1fdda-aa1c-4ee2-850d-82f36e5835c3", - "metadata": {}, - "outputs": [ - { - "ename": "ValueError", - "evalue": "x and y can be no greater than 2D, but have shapes (21,) and (21, 10, 30)", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Input \u001b[0;32mIn [35]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m plt\u001b[38;5;241m.\u001b[39mclf()\n\u001b[1;32m 2\u001b[0m ax \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39mgca()\n\u001b[0;32m----> 3\u001b[0m \u001b[43max\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mLL\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4\u001b[0m plt\u001b[38;5;241m.\u001b[39mshow()\n", - "File \u001b[0;32m/opt/conda/lib/python3.9/site-packages/matplotlib/axes/_axes.py:1632\u001b[0m, in \u001b[0;36mAxes.plot\u001b[0;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1390\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1391\u001b[0m \u001b[38;5;124;03mPlot y versus x as lines and/or markers.\u001b[39;00m\n\u001b[1;32m 1392\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1629\u001b[0m \u001b[38;5;124;03m(``'green'``) or hex strings (``'#008000'``).\u001b[39;00m\n\u001b[1;32m 1630\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1631\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m cbook\u001b[38;5;241m.\u001b[39mnormalize_kwargs(kwargs, mlines\u001b[38;5;241m.\u001b[39mLine2D)\n\u001b[0;32m-> 1632\u001b[0m lines \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_lines(\u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39mdata, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)]\n\u001b[1;32m 1633\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m line \u001b[38;5;129;01min\u001b[39;00m lines:\n\u001b[1;32m 1634\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39madd_line(line)\n", - "File \u001b[0;32m/opt/conda/lib/python3.9/site-packages/matplotlib/axes/_base.py:312\u001b[0m, in \u001b[0;36m_process_plot_var_args.__call__\u001b[0;34m(self, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 310\u001b[0m this \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m args[\u001b[38;5;241m0\u001b[39m],\n\u001b[1;32m 311\u001b[0m args \u001b[38;5;241m=\u001b[39m args[\u001b[38;5;241m1\u001b[39m:]\n\u001b[0;32m--> 312\u001b[0m \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_plot_args\u001b[49m\u001b[43m(\u001b[49m\u001b[43mthis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/opt/conda/lib/python3.9/site-packages/matplotlib/axes/_base.py:501\u001b[0m, in \u001b[0;36m_process_plot_var_args._plot_args\u001b[0;34m(self, tup, kwargs, return_kwargs)\u001b[0m\n\u001b[1;32m 498\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx and y must have same first dimension, but \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 499\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhave shapes \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mx\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m and \u001b[39m\u001b[38;5;132;01m{\u001b[39;00my\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 500\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m2\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m y\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m2\u001b[39m:\n\u001b[0;32m--> 501\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx and y can be no greater than 2D, but have \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 502\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mshapes \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mx\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m and \u001b[39m\u001b[38;5;132;01m{\u001b[39;00my\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 503\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m 504\u001b[0m x \u001b[38;5;241m=\u001b[39m x[:, np\u001b[38;5;241m.\u001b[39mnewaxis]\n", - "\u001b[0;31mValueError\u001b[0m: x and y can be no greater than 2D, but have shapes (21,) and (21, 10, 30)" - ] - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.clf()\n", - "ax = plt.gca()\n", - "ax.plot(LL[:,0])\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "9b85c517-7fcc-408c-bc68-8f85639b5201", - "metadata": {}, - "source": [ - "## Show it all" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "id": "24b700b6-1433-4a73-bde8-b5f9f9b5fd9c", - "metadata": {}, - "outputs": [], - "source": [ - "nans = np.isnan(LL)\n", - "LL_clean = LL.copy()\n", - "LL_clean[nans] = -9e9\n", - "#\n", - "LL_clean -= LL_clean.max()" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "id": "493e0416-168e-438a-9d29-40b095dbccbf", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'H0 (km/s/Mpc)')" - ] - }, - "execution_count": 59, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.clf()\n", - "ax=plt.gca()\n", - "#\n", - "im = plt.imshow(LL_clean.T, origin='lower', vmin=-4., vmax=0., cmap='jet',\n", - " extent=[F.min()-dF/2, F.max()+dF/2, 55.-dH/2, 80+dH/2], aspect='auto')\n", - "# Color bar\n", - "cbar=plt.colorbar(im,fraction=0.046, shrink=1.2,aspect=15,pad=0.05)\n", - "cbar.set_label(r'$\\Delta$ Log10 Likelihood')\n", - "#\n", - "ax.set_xlabel('F')\n", - "ax.set_ylabel('H0 (km/s/Mpc)')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1e57d2a5-f711-4e40-bcf0-4de0576ec60a", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.6" - }, - "vscode": { - "interpreter": { - "hash": "9731a3b7ebb41545a410367c249afbc4842fd5740da82f1bd29e6ac1d7253e6e" - } - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/papers/F/Analysis/CRACO/make_fig10.py b/papers/F/Analysis/CRACO/make_fig10.py deleted file mode 100644 index 6712538b..00000000 --- a/papers/F/Analysis/CRACO/make_fig10.py +++ /dev/null @@ -1,59 +0,0 @@ -""" -Plots Figure 10 ('CRACO') analysis - -Produces plots for each parameter, even though only H0 -was shown in the paper. - -""" - -import numpy as np -import os -import zdm -from zdm import analyze_cube as ac - -from matplotlib import pyplot as plt - -def main(): - - if not os.path.exists("Figure10/"): - os.mkdir("Figure10") - - CubeFile='Cubes/craco_full_cube.npz' - if os.path.exists(CubeFile): - data=np.load(CubeFile) - else: - print("Missing cube file ",CubeFile," please download") - exit() - - data=np.load(CubeFile) - - lst = data.files - lldata=data["ll"] - params=data["params"] - # builds uvals list - uvals=[] - for param in params: - uvals.append(data[param]) - - deprecated,vectors,wvectors=ac.get_bayesian_data(data["ll"]) - - latexnames=[ - "H_0", - "\\mu_{\\rm host}", - "\\sigma_{\\rm host}", - "\\log_{10} F", - ] - units=[ - "km/s/Mpc", - "", - "", - "", - ] - - # ['[erg]','[km/s/Mpc]','','','','$[\\log_{10} {\\rm DM}]',''] - - truth=[67.66,2.16,.51,-0.49] - #ac.do_single_plots(uvals,vectors,wvectors,params,tag="prior_",truth=truth,dolevels=True,latexnames=latexnames) - ac.do_single_plots(uvals,vectors,None,params,tag="Figure10_",truth=truth,dolevels=True,latexnames=latexnames,units=units) - -main() diff --git a/papers/F/Analysis/CRACO/testF.py b/papers/F/Analysis/CRACO/make_ll_2D_F.py similarity index 100% rename from papers/F/Analysis/CRACO/testF.py rename to papers/F/Analysis/CRACO/make_ll_2D_F.py diff --git a/papers/F/Analysis/CRACO/test.py b/papers/F/Analysis/CRACO/make_ll_2D_H0.py similarity index 100% rename from papers/F/Analysis/CRACO/test.py rename to papers/F/Analysis/CRACO/make_ll_2D_H0.py diff --git a/papers/F/Analysis/CRACO/marginalize.ipynb b/papers/F/Analysis/CRACO/marginalize.ipynb deleted file mode 100644 index a8b2cc2b..00000000 --- a/papers/F/Analysis/CRACO/marginalize.ipynb +++ /dev/null @@ -1,141 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import zdm.analyze_cube as ac" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "cube_dir = \"../CRACO/Cubes/craco_mini_cube.npz\"" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "cube=np.load(cube_dir)\n", - "ivalues, posteriors, weighted_posteriors=ac.get_bayesian_data(cube['ll'])" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ll\n", - "lC\n", - "params\n", - "pzDM\n", - "pDM\n", - "pDMz\n", - "pz\n", - "lEmax\n", - "H0\n", - "alpha\n", - "gamma\n", - "sfr_n\n", - "lmean\n", - "lsigma\n", - "F\n", - "lls0\n", - "P_zDM0\n", - "P_n0\n", - "P_s0\n", - "N0\n" - ] - } - ], - "source": [ - "for key in cube.keys():\n", - " print(key)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "h0_idx = np.where(cube[\"params\"] == \"H0\")[0][0]" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(dpi=200)\n", - "ax.plot(cube[\"H0\"],posteriors[h0_idx], c=\"k\")\n", - "ax.set_xlabel(\"$H_0$\")\n", - "ax.set_ylabel(\"$p(H_0)$\")\n", - "ax.set_ylim(0, 0.05)\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5" - }, - "vscode": { - "interpreter": { - "hash": "9731a3b7ebb41545a410367c249afbc4842fd5740da82f1bd29e6ac1d7253e6e" - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/papers/F/Analysis/CRACO/py/craco_qck_explore.py b/papers/F/Analysis/CRACO/py/craco_qck_explore.py index 1aab71fd..6d78228c 100644 --- a/papers/F/Analysis/CRACO/py/craco_qck_explore.py +++ b/papers/F/Analysis/CRACO/py/craco_qck_explore.py @@ -22,26 +22,13 @@ def main(pargs): elif pargs.run == "F": scube = "H0_F" outdir = "H0_F/" - elif pargs.run == "lmF": - scube = "lm_F" - outdir = "lm_F/" elif pargs.run == "H0_logF": scube = "H0_logF" outdir = "H0_logF/" + # Main # elif pargs.run == "logF_full": scube = "full" outdir = "logF_Full/" - elif pargs.run == "full": - scube = "full" - outdir = "Full/" - elif pargs.run == "full400": - scube = "400_full" - jroot = "full" - outdir = "Full400/" - elif pargs.run == "full3rd": - scube = "3rd_full" - jroot = "full" - outdir = "Full3rd/" if jroot is None: jroot = scube @@ -63,6 +50,46 @@ def main(pargs): # Deconstruct the input_dict state_dict, cube_dict, vparam_dict = it.parse_input_dict(input_dict) + latexnames = [] + for ip, param in enumerate(npdict["params"]): + if param == "alpha": + latexnames.append("$\\alpha$") + ialpha = ip + elif param == "lEmax": + latexnames.append("$\\log_{10} E_{\\rm max}$") + elif param == "H0": + latexnames.append("$H_0$") + elif param == "gamma": + latexnames.append("$\\gamma$") + elif param == "sfr_n": + latexnames.append("$n_{\\rm sfr}$") + elif param == "lmean": + latexnames.append("$\\mu_{\\rm host}$") + elif param == "lsigma": + latexnames.append("$\\sigma_{\\rm host}$") + elif param == "logF": + latexnames.append("$\\log_{10} F$") + + units = [] + for ip, param in enumerate(npdict["params"]): + if param == "alpha": + units.append(" ") + ialpha = ip + elif param == "lEmax": + units.append("[$\rm erg$]") + elif param == "H0": + units.append(r"[$\rm km \, s^{-1} \, Mpc^{-1}$]") + elif param == "gamma": + units.append("") + elif param == "sfr_n": + units.append(" ") + elif param == "lmean": + units.append(r"[$\rm pc \, cm^{-3}$]") + elif param == "lsigma": + units.append(r"[$\rm pc \, cm^{-3}$]") + elif param == "logF": + units.append(" ") + # Run Bayes # Offset by max @@ -71,7 +98,16 @@ def main(pargs): uvals, vectors, wvectors = analyze_cube.get_bayesian_data(ll_cube) analyze_cube.do_single_plots( - uvals, vectors, wvectors, params, vparams_dict=vparam_dict, outdir=outdir + uvals, + vectors, + None, + params, + vparams_dict=vparam_dict, + outdir=outdir, + compact=True, + latexnames=latexnames, + units=units, + dolevels=True, ) print(f"Wrote figures to {outdir}") @@ -79,7 +115,7 @@ def main(pargs): def parse_option(): """ This is a function used to parse the arguments in the training. - + Returns: args: (dict) dictionary of the arguments. """ @@ -96,7 +132,6 @@ def parse_option(): # Command line execution if __name__ == "__main__": - pargs = parse_option() main(pargs) diff --git a/papers/F/Analysis/Real/cube_diag.ipynb b/papers/F/Analysis/Real/cube_diag.ipynb deleted file mode 100644 index 9f31e45c..00000000 --- a/papers/F/Analysis/Real/cube_diag.ipynb +++ /dev/null @@ -1,1377 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import zdm.analyze_cube as ac\n", - "import matplotlib.pyplot as plt\n", - "import zdm.analyze_cube as ac" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## inspecting cubes" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "cube_dir = \"../CRACO/Cubes/craco_full_cube.npz\"\n", - "cube_dir_real =\"./Cubes/craco_real_cube.npz\"\n", - "\n", - "cube=np.load(cube_dir)\n", - "cube_real=np.load(cube_dir_real)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Marginalization over H0\n", - "fig, ax = plt.subplots(5,5, dpi=200, figsize=(15,15))\n", - "\n", - "H0s = np.linspace(75, 100, 25)\n", - "\n", - "for H0, a in zip(H0s, ax.flatten()):\n", - " ll = ac.get_slice_from_parameters(cube, [\"H0\", \"lmean\", \"lsigma\"], [H0, 2.16, .51], wanted=\"ll\")\n", - " ll_real = ac.get_slice_from_parameters(cube_real, [\"H0\", \"lmean\", \"lsigma\"], [H0, 2.16, .51], wanted=\"ll\")\n", - "\n", - " \n", - " _, vectors, _= ac.get_bayesian_data(ll)\n", - " _, vectors_real, _ = ac.get_bayesian_data(ll_real)\n", - "\n", - " ll[np.isnan(ll)] = -1e99\n", - " ll -= np.max(ll)\n", - " ll = 10**ll\n", - " ll /= np.sum(ll)\n", - " ll_real[np.isnan(ll_real)] = -1e99\n", - " ll_real -= np.max(ll_real)\n", - " ll_real = 10**ll_real\n", - " ll_real /= np.sum(ll_real)\n", - "\n", - " a.plot(cube[\"logF\"], vectors[-1], c=\"b\", label=\"Synth\")\n", - " a.plot(cube[\"logF\"], vectors_real[-1], c=\"r\", label=\"Real\") \n", - "\n", - " a.plot(cube[\"logF\"], ll, c=\"b\", ls='--', alpha=.5)\n", - " a.plot(cube[\"logF\"], ll_real, c=\"r\", ls='--', alpha=.5) \n", - "\n", - " a.set_xlabel(\"log F\")\n", - " a.set_ylabel(\"ll\")\n", - " a.text(.05, .925,f\"H_0 = {np.round(H0,3)}\", transform=a.transAxes)\n", - "\n", - " if H0 == H0s[0]:\n", - " a.legend(loc=\"lower left\")\n", - "\n", - "fig.suptitle(\"bayesian\")\n", - "fig.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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nF5877rhjr210FDAB6KgJREc1Gsr3Uf259NJLc++99+bFL35xNtlkk+LxLjN//vycfPLJ2W677bLiiitm9dVXz9SpU/OUpzwl73nPe1rWa6NBRwETgI6aQHRUo2Y66sgjj0ySPPLIIzn11FN7rV+yZEmOO+64JJ2Tsffff/9hjfvhhx/OZz/72Wy55ZZZaaWVujpqu+22y0c/+tF+J0T1dMYZZ+Tqq6/OkUcemW233XZYY+pL9557wQte0Oc2OgqYAHTUBKKjGg3WUd2voD7QRTkXLVrU9fzhhx/O7Nmzi8c93I464ogjMnny5CTJscce2+c2y5ZPmjQpb3nLW4rG+clPfjKPPfZYZsyYkc985jN9bqOjgGUm6j1Lr0yy7L4jO6TzjLy+PLPb86Gc+nVekl8Nss2RSV6y9Pmbkty39Pn8IRynrS1ZsiQPPvhgU9suWLBgRMawYMGC3Htv58mO66478Imd3dfffvvtRcc755xzcvDBBxe9t7uSqwUknROArrjiirzxjW/MH/7wh7zwhS/Mxz72sWy11Va5//77853vfCf/7//9vyTJYYcdlsMPP3zA/R188MF53vOely984QvZZJNNMnfu3PzkJz/JBRdckEsvvTSvec1rct555/V79YBmHXbYYeno6MjFF1/c721c2sGZZ56ZJHn729/e7zY9IyvpvAXNppuO1KgARp2OGgU6qtxYdtTf/va3rueXX355vvzlL2fzzTfPJz/5yWy66aa58847c9ppp+WMM87IBRdckG9+85vZa6+9yj7oUq3qqCOOOCJPf/rTc/TRR2fLLbfM448/nl/+8pf56le/mp/97Gf5n//5n3znO9/J2muv3ef7jz/++EyZMiUnnnhi9txzz3zmM5/JC1/4wkydOjXXXHNNPvKRj+TRRx/Ntttum4svvrjPu9joKGAC0FGjQEeVa5fvo/pzxhlnJBn4e5mhuOaaa/KnP/0pBx54YI466qisuuqqufXWW3POOefkpJNOyplnnpnTTjstBx10UEuON5J0FDAB6KhRoKPKjXVHve51r8vXvva1vOc978n73//+3H333Xnta1+bddddNzfeeGM+/elP57rrrssaa6yRCy+8sGvCVqnvf//7+cUvfpFDDz00z3ve8zJt2rTceOONOeOMM/K5z30up556ar7+9a/n5S9/eb/7uOeee/KhD30oW265ZY4++uhhjac/l1xySdfzd7zjHX1uo6OACUBHjQIdVW4sO2rLLbfsev6vf/0rz3nOc/o8Vs+f0YMPPphZs2YVjXu4HbXzzjvn+9//fg4++OCccMIJmTt3bg455JBsuOGGufXWW3PCCSfk0ksvzbRp03Laaac1XPW+P0uWLMmcOXPy73//O3/7299y1lln5bLLLssOO+yQiy66KFtttVWf79NRQJe6rifcI8lTk9RLH58aYLurl25zV5KqxWM4p9sYNhnBz7rBsuPcdddd9WjYeOON626fbciP2267rWVjeeCBB7r2+/SnP33AbTs6Orq23XbbbYuOd/bZZw/rsy97DNeiRYvq008/vV5vvfUa9jt58uR6//33r6+88soB33/llVfWSepjjjmmXrJkSa/1P/7xj+spU6bUSerXvOY1wxrrJZdcUiepDzrooD7HkKT+xCc+MeT9Lvt3uPHGGw9rfMv88pe/rJPUO+6444DbHXVUXSeNj1//uiVDAEbAXXfd1f2/kxvUbdAp7f7QUSNLR43vjjryyCMb3vPc5z63nj9/fsM2CxYsqF/4whfWSepp06bV119/ffFYW9FRt912W52kfstb3lIvWrSo1/rrrruuXnXVVesk9c4771wvWLBgwP394Q9/qF/0ohf1+nvZfvvt69NPP73Xz6M7HQXji47SUYMcR0fpqCF11GBuu+22etKkSfWsWbP6bJahOvDAA+uZM2fW1113Xa91ixcvrt/85jfXSeqqqupLLrlk2McbaToKxhcdpaMGOY6O0lHFHTV79uz64IMPridPntywn3XXXbf+1Kc+Vd99993DHuuuu+5ab7LJJvWtt97aa938+fPrPffcs05Sr7jiivXvf//7fvezzz771El6fbYDDzywJf+2Hn/88fo//uM/6iT1fvvt1+92OgrGFx2lowY5jo7SUUPqqCuuuKJr+wMOOKDf7b7whS807PvXhbHQqo6q67p++OGH6/e///31Siut1DC2VVddtf7gBz9Y33TTTU2Pa9nvCpc9Vl555fqYY44Z8Hd6da2jYLwZyY6akFdCr+v6pqqqvpNk3yQHVFX1mbquF3bfpqqqrZI8f+nLY+u6s1i6rV8/yaVJNknyzrquLx75kY8v6667bi644IKmtj3uuOPy05/+tOVjmD//yRMnV1hhhQG37b5+3rx5Rcd79atfnZ133rnova1y8803553vfGeuuOKKbLPNNjnqqKOyxRZb5OGHH873v//9XHzxxZk7d25WX3317LDDDn3uY+edd84dd9yRjTbaqM/1L3vZy/KOd7wjJ598cr773e/mhz/8Yf7zP/9zyGP997//nXe9611ZZ511us5AbFfNXm2rrzP97rlnJEYEMDZ01OjQUWNjuB31yCOPNLw+8cQTs9JKKzUsW2GFFXLaaaflaU97WubPn5+PfvSjDVdlalarOmqDDTbIbbfdlo022iiTJk3qtf6Zz3xmPv7xj+cDH/hAfve73+XMM8/Mu9/97l7bLVq0KEcffXROOumkJJ1XVn/+85+fKVOm5Nprr80pp5ySL3/5y5k0aVIOPfTQpq+ErqOA5YmOGh06amy04vuogXz1q1/NkiVLcsghh2TKlOF/pf2lL30pxx9/fNZYY41e6yZNmpRTTz01P/nJT3LfffflsMMOy8te9rKsuOKKwz7uSNFRwPJOR40OHTU2WtVR559/fj70oQ/l3nvvzete97q86lWvyuqrr55//vOfOf3003PSSSdl3rx5+chHPpIZM2YUj/cb3/hGpk+fnlVXXbXXupVWWinnnntuNttssyxYsCCHHXZYrrnmml7bffvb384ll1ySt7zlLXnRi15UPJaBfP7zn8///d//ZYMNNsiJJ57Y73Y6Clje6ajRoaPGxnA7arfddssuu+ySX//61/nGN76RI444otd2999/f44//viGZZMnTy4abys6Kum8G/Phhx+ef/7zn3npS1+a//qv/8q6666bO++8M+ecc05OP/30PPLIIznmmGOyzjrrDDqu9dZbLz/72c+ycOHC3HnnnbnssstyzDHH5Ctf+Uo+/vGP573vfW+f79NRQJdWzmgfT490xtGD6ZzZ/8Ue66blybP8fpNkah/v/3yePDPgnoLjn9Pt/cvlmX5DuQJ1q85q72m0z/Qba//85z/rNddcs05S77HHHvXjjz/ea5szzjij68y5H/zgB8XH+tOf/tT189pnn32K9vH2t7+9TlJ//etf77Wuna6EPmfOnHqllVaqZ8yYUT/66KMDbvv619e9zvT7yleGPQRghLhigo4a5Dg6Skc1GKyjDj300K6fw2B/h9ttt12dpJ40aVI9d+7cIY93JDuqpzlz5tRVVdVJ6h122KHX+iVLlnRdvWrllVeu//SnP/Xa5vbbb69nzZpVJ6nf9KY39XkcHQXji47SUYMcR0fpqAbD+T5q0aJF9axZs+pJkya19O9pMP/93//d9XfT7ldD11EwvugoHTXIcXSUjmrQTEedcMIJXT+LM888s9f6+fPn17vvvnvXz/Whhx5q+Wfp7lWvelXXeP785z83rOvo6KjXW2+9et111+1zHK34t3X11VfXU6ZMqadNm1Zfc801A26ro2B80VE6apDj6Cgd1aCZjrrzzjvrzTbbrE5Sr7XWWvXpp59e33rrrfUdd9xRf/Ob36y32GKLet1116133HHHrp/ZP/7xjxH7XAN1VF3X9be//e160qRJdZL6ox/9aK/1ixcvrg844IA6Sb3BBhv0edX1Znzzm9/sOs673vWuPrfRUTC+jGRH9b7U3QRR1/XtSfZOcl+SI6uq+nFVVe+qquoDSf6Q5AVL/9ynrutFfeyi+8+u92X8+lBV1T5VVb2pqqo3Jdms26qu5VVVbdbf+xm67mfyP/744wNu2/2swOFcAWAsHX744ZkzZ06qqsr//M//9HmFpre+9a3Zdddds2DBghxwwAF5+OGHi4617bbbdu3/17/+9ZDf/7//+78544wz8vKXvzxvfOMbi8YwWs4999w8/vjj2X///bPyyisPuK0z/YCJQEdNDDpq6B21yiqrdD1/+tOfPuDxll1JYcmSJbnuuuuGNNbR7qg11lgjm23W+T+vv/zlL3n00Ucb1l988cVdV3Pv6yoRSbLxxhvnc5/7XJLkggsuyLnnnttrGx0FTAQ6amLQUa39Puqyyy7L7Nmz85KXvCSbbLJJ6wY+iB133LHrecl3X6NJRwETgY6aGHTU0Dvq7rvvzgc/+MEkyQte8IIceuihvfax0kor5cwzz8ykSZNy/fXX54gjjhiRz7PMQB31gQ98IPfee2++8pWvZObMmS0/9i233JJ99903SfLNb34zO+2004Db6yhgItBRE4OOKvs+asMNN8y1116bww8/PIsWLco73vGObLbZZtl4441z4IEHZuedd84f/vCHbLTRRl3vWXvttUfscw3UUfPmzcs73vGOLFmyJJtuumk++clP9nr/pEmTcsopp2TGjBm5++67c8ghhxSN4/Wvf33e9ra3JUlOPfXU/OxnP+u1jY4Clhn+vUvHsbquf1tV1XZJ3ptknyTHJVmY5MYkhyf5n34CK0lOSvLiJBsleU+Th/xyko37WH5Ct+cHJ7m1yf0xiBVXXDHrrbde7r333tx3330Dbtt9/cYb9/XXNLi5c+dm9uzZRe/tbqutthryex566KFcfvnlSTonPW2++eb9brvPPvvkl7/8ZTo6OnLxxRfnrW9965CPN3ny5KyxxhqZPXt2HnzwwTzxxBNN3w554cKFeetb35oVVlghn/rUp/Lggw/22mbu3Lldz+fNm9ewzWqrrZapU6cOecylzjzzzCTJ29/+9kG37SuyWvBPAqDt6Kjln47q20AdtdZaa3U9X3311Qc85pprrtn1/P777296rGPVUeuss07+9a9/pa7r3HfffQ0T7i+88MKu5/vss0+/+3jVq16VqqpS13XOOOOMHHjggQ3rdRQwUeio5Z+O6lvp91FnnHFGkua+l2ml7rcrbsXPdyTpKGCi0FHLPx3Vt4E66uKLL86iRYu6tuvPZpttlm233TZ/+ctfcuGFF+bkk08e9MJLpfrrqF/+8pc566yzsuuuu2aPPfbo83utBQsWdD3v6Ojo+g5q8uTJg05av/POO/PiF784HR0dueiii7L33nsPOlYdBUwUOmr5p6P61sz3UWussUZOPPHEnHDCCbnxxhszZ86czJgxI0996lMzffr0JJ1dkiTrrbdew+8DW22g76N+8pOfdPXTXnvtlcmTJ/e5jxkzZmT33XfP97///Vx11VW55ZZbsuWWWw55LAcccEBOP/30JMnZZ5+dF7/4xQ3rdRSwzISehJ4kdV3fn+QjSx9Ded/dSZ45xPdsMpTtaY2nPe1puffee/PII49k7ty5WW211frc7u677254T4nvfe97Ofjgg4ve213deaugIbnlllu63jfYVaE23XTTrud//etfh3ysZZYsWZIkqaoqkyY1f2OFe+65JzfddFOSDHoFgiQ57rjjctxxx3W9vvLKK/OiF71oaIMt9L//+7+54YYbstNOO/V5Vc+enOkHTCQ6avmno3obqKO22WabrudPPPHEgPvpPs7+viTqy1h11LLuS3qP9+abb+56PtDPb+bMmVlttdXy8MMP99mgOgqYSHTU8k9H9VbyfdQdd9yRn/70p1l//fWz1157DXl8wzFQ/7QbHQVMJDpq+aejehuoo5r9XmbZfv7yl79k0aJFufHGG/OsZz1raINuUn8ddeWVV6au6/zyl79s6gqiz3zmk/+T3XjjjXP77bf3u+3dd9+d3XbbLXfffXe+/vWv57WvfW1TY9VRwESio5Z/Oqq3oXwfNXny5H5/Hrfe2nm+xEj10zKt+H1c0vtzl0xCf+pTn9r1/Prrr++1XkcBy0z4Segs/3bbbbf84he/SJL8+c9/zq677trndn/84x+7nu++++6jMrZW6j4JfLBI6x4tixcvblg3d+7cnHTSSXnBC17Q788qSRYtWpSHlhbFOuusM6RJ6Outt16ft2rp7i9/+Us+8IEPJOk8u+7Nb35z17rtt9++6WMN11CvtiWyAFie6KjeBuqo7pPCB7vKxAMPPND1fP31129qnEnrO+ozn/lMtt1227zqVa8acJ/33ntvks6fVferMCxbtkyzP7+eP7tERwGwfNFRvQ3UUf356le/miVLluSQQw5p+g58g7n66qtz9dVX593vfveAV9Nc1j9JMmvWrJYce6ToKACWJzqqt4E6aqR7rLvLLrssf/nLX/LRj340VVX1u11/HfXmN785z3/+8wc8xnHHHZef/vSnSZILLrgg6667bpJk2rRp/b5n9uzZ2X333XP77bfn/PPPz+tf//qmPk+iowBYvuio3obbP0nn7/zuvPPOJGn6RLeehttRSes+9x/+8Ifcfvvtg36W7t/F9XXxLR0FLNP8rFEYp7r/n+ay2OrLz3/+8yTJBhtskJ133rnoWAcddFDquh72o0T3W+T861//GnDbZWfoJclGG23UsK6joyNHH310LrroogH3cd1113Xd3m+wL4x6WmmllbLnnnsO+Oh+9uBmm23WsG6w2+21SkdHR7797W9n1VVXzX/9138Nuv2iRcm//917udvNADBe6ajeBuqoDTbYIM95znOSdH65N9DV0Jd9wTd9+vTsuOOOTY+11R119NFH57TTThvwmLNnz84dd9yRpPMKVMtuPbhM96stDPTze/DBB/PvpbHU82enowBY3uio3gbqqL4sXrw4Z511ViZNmpRDDz20aHx9ueKKK3L00Ud33V2mP7/73e+6ng/1u6/RpKMAWN7oqN4G6qhmv5fpuZ8NN9ywmWE2+M53vpOjjz560Isv9NdRPb+n6uvRfbLVLrvs0rV8l1126fNY9913X3bffff861//yjnnnJP99tuv1zZnnHFGdtxxx8zuEUg6CoDljY7qbbDvo6699tr8+Mc/HnAfl112Weq6zuqrr57XvOY1Qxxtp+F2VFLefT0/98knn5zXve51efDBBwfcxy233NLvPnQU0J1J6Cz3nvrUp2bfffdNkpx//vlZuHBhr21uvPHG/OpXv0qSfPjDHx7wrLN2tc4663RNerrhhhvy97//vd9tv/Wtb3U9f/nLX97nNj/96U8HPAuw+2Slt73tbX1uc8QRR2TVVVfN/vvv33CW3Xhx3nnn5fHHH8/++++flVdeedDtH3647+UdHcn8+a0dGwCMBh3V22AddcQRRyRJHn744fzgBz/ocx9//OMfc8MNNyRJDj744Kywwgp97me0Ouo3v/lN1+Twvpx++uldz/vqvr333rvr+be//e1+93PxxRd3PX/FK17RsE5HAbC80VG9NfN9VHc/+MEPcs899+SlL31pwy8XB3P88cdn9dVXz0te8pLMmzev3+0G+gXjQw891DXejTfeOC95yUuaPv5o01EALG90VG8DddRee+3V9fkH+l7mH//4R66//vokyTOe8Yw+7/TS7PdRA3XULbfc0jWx7XnPe16e9rSn9bvtcD3wwAPZY489ctNNN+WrX/1qDjjggD63u+eee3LddddlwYIFDct1FADLGx3V22DfR33hC1/IK17xioYrkHe3YMGCfOlLX0qSfOpTn8qqq67a53aj0VF77rln191hLr300j7/fpNkzpw5XSchrL322v1eDOtHP/pRv2NJknPOOafr+V577dWwTkcB3ZmEzoTwpS99KWuuuWZuv/32fOxjH2tYN3/+/LztbW9LXdd57nOf2++E6vHg85//fCZPnpwkectb3tLnZKIvfOELueaaa5Ikb3zjG7P99tv3ua/bbrstH/zgB/s88/Dcc8/N+eef37WPF7/4xb22uemmm3LCCSfkkUceyde//vWuUBpPzjzzzCT9T7Lvqa9bzSzjbD8Axisd9aRmOur1r399XvaylyVJ3ve+9/W6wtIjjzzS9XPaeOON88lPfrLXPka7o5aNqa8vq37605/mC1/4QpLkhS98YQ4++OBe2xx88MHZaqutkiQnnnhirr766l7b/OMf/8jRRx+dJFljjTVy5JFHNqzXUQAsj3TUk5r9Pqq7M844I0nz38skyaOPPpoPfehDmTt3bn72s5/lggsu6Hfb//f//l9+85vf9Fo+f/78HHDAAeno6MikSZNy2mmnZerUqU2PYbTpKACWRzrqSYN11NZbb52DDjooSeeVPJd9j9Pdv//97xxyyCFJkqqq8rnPfa7XNkP5Puqoo47KzTff3Gv5Qw89lDe+8Y1ZvHhxpk+fnlNOOaXffQzXnDlzsueee+bvf/97DjjggGy66aa56qqr+nzcfvvtfe5DRwGwPNJRTxrK91FHHXVUr2WPP/54DjrooNx000159atfnXe96119vne0OmrmzJn58Ic/nCT5v//7v7zvfe/rNadr4cKFOeSQQzJ/6Uzwz3zmM5kyZUqfY/ngBz+Y6667rs91559/ftfFSbfaaqu89a1v7THefj+ijoIJqO//ysAQ/fWvf81f//rXJMljjz3W9eeyX/Q873nPy2abbdbwnvvuuy8/+9nPkjTeBuSSSy7JWmutlc033zzPfe5zWzK+TTbZJJdddlle/epX57jjjsvf/va37L333pk3b17OPvvs/OMf/8iOO+6YSy65pK1/qTSY3XbbLeeee27e/va35/e//3222WabHHjggdliiy3y8MMP59JLL81VV12VJHn1q1+dr33ta732scoqq2TzzTfPv/71rxx//PG54oorsu+++2aDDTZIR0dHfvzjH3f9vR100EENV0TvrueZfc3cRqf7v6NlVwddtnzZv6V11123z0nvt956a8MvDvv6d5j0/W+xL7/+9a/z97//Pc9+9rOzww47DLp90nlGX39mz06aOCwAE5COag+t6KhlLr744uyzzz75xS9+ke233z6HHnpotthii9x9990566yzcscdd2SrrbbKpZdemjXXXLPX+0ezo7bffvv85S9/yTe/+c1cd911+a//+q9sttlmeeyxx3LVVVflu9/9buq6zstf/vJceOGFfX5RteKKK+byyy/PPvvskz//+c/ZY4898oY3vCHPf/7zM3ny5Fx33XU577zzMn/+/Gy44Yb5zne+0+tqWzoKgBI6qj20sqOWueuuu3L55Zdn/fXX73WlpcF0b6e+OmrLLbfM9OnT89hjj2XXXXfNa17zmrzgBS/IyiuvnH/961+54IILcscdd2TGjBk566yzmrpq+1jSUQCU0FHtoVUdddppp2XevHn55je/mQ9/+MO5/PLL88pXvjKrr756/vnPf+acc87JPffck2nTpuXkk0/uuoBCd818H7XNNttk6tSpue+++7LDDjvkv/7rv7LTTjtlhRVWyA033JDzzjsvDzzwQNZdd91885vfbPr3a5dcckkeffTRJH3/20p6/5vcd999u/4Nn3feeTnvvPOaOlZ3OgqAEjqqPbT6+6hzzjknN954Y/bZZ5+sueaaufXWW3PRRRfl9ttvz6GHHppTTjmla9J7T6PZUUcffXQ6Ojryla98Jaeeemp+//vf53Wve13WWWed3H333bngggty8803Z/LkyTnmmGP6PNGg+1h22mmn7L333tlxxx3zH//xH5kzZ05+9KMfdf3snvOc5+Q73/lOVlxxxYZ96CigQV3XHsvxI8kGSeok9V133VWPlE984hP1suP09Tj77LN7vefKK68c8D0HHnhgy8d533331UcddVS99dZb19OnT69XX331euedd65POumkeuHChS0/3li5++67649//OP1c5/73HqNNdaop0yZUq+88sr1lltuWR9wwAH1T3/60wHfv2TJkvoXv/hF/d73vrfeeeed6zXXXLOeMmVKvcoqq9RPfepT60MPPbT+7W9/O+g43vOe99SrrLJKvd9++9WLFy8edPvB/h0lqXfdddc+33v22WcP+t7+/i325c1vfnOdpP7qV7/a1PZ1Xdc//GFdJ30/vvWtpncDjKK77rqr+38jNqjb4P+7PdrnoaMa6ajmOmqZJUuW1BdeeGH90pe+tF533XXrqVOn1muttVa9++6716eddlq9YMGCAd8/mh31u9/9rj7qqKPqF7zgBfU666xTT506tZ4+fXq92Wab1fvvv399+eWXN/WZFy5cWJ9//vn1K1/5ynrDDTesV1xxxXqFFVao11tvvfrFL35xfdJJJ9X//ve/+3yvjoLxR0d5DPTQUY101NA6qq7r+uMf/3idpP7Yxz425HF88YtfrFddddX6xS9+cf3oo4/2uU1HR0d99tln1294wxvqrbbaql5llVXqKVOm1GuttVa9yy671J/+9Kfr+++/f8jHHgs6CsYfHeUx0ENHNdJRQ+uoK6+8sj7wwAPrpz71qfXKK69cT5kypV5jjTXqnXfeuf7oRz9a33HHHQO+v5nvo2bPnl2fcsop9T777FNvvvnm9fTp0+upU6fW66yzTr3HHnvUJ5xwQr/f//Rn4403HvLv95p5T8/Hbbfd1rAPHQXjj47yGOihoxrpqOY66m9/+1v92c9+tt5zzz3rTTfdtJ4+fXq9yiqr1E95ylPqt7/97U3Njarr0e+o6667rn7nO99Zb7vttvWMGTPqyZMn16uttlr9jGc8o37ve99b//3vfx/w/ffcc0998skn169+9avrLbfcsl5llVXqyZMn1zNmzKi32mqrev/996+///3v9/tZdBSMPyPZUVXd+X/ELKeqqtogyV1J5xWENthggzEeESy/LrggOeCAvtd9+cvJf//3qA4HaMLdd9+dDTfccNnLDeu6vnssx0N70VEwenQUjD86ioHoKBg9OgrGHx3FQHQUjB4dBeOPjmIgOgpGj46C8WckO2pSq3YEMNE99FD/6+65Z/TGAQAw3ugoAIAyOgoAoIyOAgAoo6OA7kxCB2iRgSJr9uzRGwcAwHijowAAyugoAIAyOgoAoIyOArozCR2gRZzpBwBQRkcBAJTRUQAAZXQUAEAZHQV0N2WsBwCDeeCBB7J48eIhv2+99dYbgdFA/0QWAO1GRzFe6CgA2o2OYrzQUQC0Gx3FeKGjAGg3OorxQkcB3ZmETtvbaaedcscddwz5fXVdj8BooH9uNwNAu9FRjBc6CoB2o6MYL3QUAO1GRzFe6CgA2o2OYrzQUUB3JqHT9i688MLMnz9/rIcBgxoosh5+OJk3L5k+fdSGAwA6inFDRwHQbnQU44WOAqDd6CjGCx0FQLvRUYwXOgroziR02t4uu+wy1kOApnR0DLx+9uxk881HZywAkOgoxg8dBUC70VGMFzoKgHajoxgvdBQA7UZHMV7oKKC7SWM9AIDlxUBn+iVuOQMA0B8dBQBQRkcBAJTRUQAAZXQU0J1J6AAtsGTJ4JF1zz2jMxYAgPFERwEAlNFRAABldBQAQBkdBfRkEjpACzzySGdodbfJJo2vRRYAQG86CgCgjI4CACijowAAyugooCeT0AFaoK+z/J7+9MbXIgsAoDcdBQBQRkcBAJTRUQAAZXQU0JNJ6AAt0DOypkxJnvKUxmWzZ4/eeAAAxgsdBQBQRkcBAJTRUQAAZXQU0JNJ6AAt0DOy1lgjWX/9xmXO9AMA6E1HAQCU0VEAAGV0FABAGR0F9GQSOkALiCwAgDI6CgCgjI4CACijowAAyugooCeT0AFaoKOj8fXMmcmsWY3L3G4GAKA3HQUAUEZHAQCU0VEAAGV0FNCTSegALdDMmX5z5yaPPTZ6YwIAGA90FABAGR0FAFBGRwEAlNFRQE8moQO0QF+R1fNMv8TZfgAAPekoAIAyOgoAoIyOAgAoo6OAnkxCB2iBviJrxoxklVUal4ssAIBGOgoAoIyOAgAoo6MAAMroKKAnk9ABWqCvyEp633LmnntGZzwAAOOFjgIAKKOjAADK6CgAgDI6CujJJHSAFhBZAABldBQAQBkdBQBQRkcBAJTRUUBPJqEDtIDIAgAoo6MAAMroKACAMjoKAKCMjgJ6MgkdoAX6i6xZsxqXz549OuMBABgvdBQAQBkdBQBQRkcBAJTRUUBPJqEDtIAz/QAAyugoAIAyOgoAoIyOAgAoo6OAnkxCBxim+fOTBQsal82c2fmnyAIA6J+OAgAoo6MAAMroKACAMjoK6ItJ6ADD1PMsv8TtZgAAmqGjAADK6CgAgDI6CgCgjI4C+mISOsAw9RVZq6/e+WfPM/3+/e/k0UdHfEgAAOOCjgIAKKOjAADK6CgAgDI6CuiLSegAw9QzslZfPZk8ufN5zzP9Emf7AQAso6MAAMroKACAMjoKAKCMjgL6YhI6wDD1jKxlt5pJklVWSWbMaFwvsgAAOukoAIAyOgoAoIyOAgAoo6OAvpiEDjBMA0VW0vuWM/fcM7LjAQAYL3QUAEAZHQUAUEZHAQCU0VFAX0xCBxgmkQUAUEZHAQCU0VEAAGV0FABAGR0F9MUkdIBhElkAAGV0FABAGR0FAFBGRwEAlNFRQF9MQgcYpo6Oxtc9I2vWrMbXs2eP7HgAAMYLHQUAUEZHAQCU0VEAAGV0FNAXk9ABhqnnmX4zZza+dqYfAEDfdBQAQBkdBQBQRkcBAJTRUUBfTEIHGCa3mwEAKKOjAADK6CgAgDI6CgCgjI4C+mISOsAwDRZZbjcDANA3HQUAUEZHAQCU0VEAAGV0FNAXk9ABhmmoZ/o98kjnAwBgotNRAABldBQAQBkdBQBQRkcBfTEJHWCYhnqmX+JsPwCAREcBAJTSUQAAZXQUAEAZHQX0xSR0gGFYtKj3WXs9I2vllZNVV21cJrIAgIlORwEAlNFRAABldBQAQBkdBfTHJHSAYejo6L2sZ2QlvW85c889IzMeAIDxQkcBAJTRUQAAZXQUAEAZHQX0xyR0gGHoeauZJJk5s/cykQUA0EhHAQCU0VEAAGV0FABAGR0F9MckdIBh6Hmm3/TpyYor9t5u1qzG1243AwBMdDoKAKCMjgIAKKOjAADK6CigPyahAwxDzzP9+rrVTOJMPwCAnnQUAEAZHQUAUEZHAQCU0VFAf0xCBxgGkQUAUEZHAQCU0VEAAGV0FABAGR0F9MckdIBhEFkAAGV0FABAGR0FAFBGRwEAlNFRQH9MQgcYhmYja9asxtezZ4/MeAAAxgsdBQBQRkcBAJTRUQAAZXQU0B+T0AGGofRMv0cfTR55ZGTGBAAwHugoAIAyOgoAoIyOAgAoo6OA/piEDjAMpWf6JW45AwBMbDoKAKCMjgIAKKOjAADK6CigPyahAwxDs5E1fXqy2mqNy9xyBgCYyHQUAEAZHQUAUEZHAQCU0VFAf0xCBxiGjo7G1/1FVtL7ljPO9AMAJjIdBQBQRkcBAJTRUQAAZXQU0B+T0AGGoeeZfjNn9r+tyAIAeJKOAgAoo6MAAMroKACAMjoK6I9J6ADD0OztZpJk1qzG1243AwBMZDoKAKCMjgIAKKOjAADK6CigPyahAxRassTtZgAASugoAIAyOgoAoIyOAgAoo6OAgZiEDlDo3//uDK3uRBYAwOB0FABAGR0FAFBGRwEAlNFRwEBMQgco1PNWM8nQbjcjsgCAiUpHAQCU0VEAAGV0FABAGR0FDMQkdIBCPSNr6tRk5ZX7377nmX6zZyd13fpxAQC0Ox0FAFBGRwEAlNFRAABldBQwEJPQAQr1jKw11kiqqv/te0bWY48ljzzS+nEBALQ7HQUAUEZHAQCU0VEAAGV0FDAQk9ABCnV0NL4e6FYzSe/bzSRuOQMATEw6CgCgjI4CACijowAAyugoYCAmoQMU6utMv4FMm5asvnrjstmzWzokAIBxQUcBAJTRUQAAZXQUAEAZHQUMxCR0gEI9I2vmzMHf0/OWM870AwAmIh0FAFBGRwEAlNFRAABldBQwEJPQAQoN9Uy/RGQBACQ6CgCglI4CACijowAAyugoYCAmoQMUKomsWbMaX7vdDAAwEekoAIAyOgoAoIyOAgAoo6OAgZiEDlDImX4AAGV0FABAGR0FAFBGRwEAlNFRwEBMQgcoJLIAAMroKACAMjoKAKCMjgIAKKOjgIGYhA5QqBW3mxFZAMBEpKMAAMroKACAMjoKAKCMjgIGYhI6QKFWnOk3e3ZS160bEwDAeKCjAADK6CgAgDI6CgCgjI4CBmISOkCBuk46OhqXlUTWvHnJv//dunEBALQ7HQUAUEZHAQCU0VEAAGV0FDAYk9ABCsyfnyxY0Lhs5szB39fzdjOJW84AABOLjgIAKKOjAADK6CgAgDI6ChiMSegABXreaiZp7ky/lVbqHWOzZ7dmTAAA44GOAgAoo6MAAMroKACAMjoKGIxJ6AAFekZWVSWrrdbce3vecsaZfgDARKKjAADK6CgAgDI6CgCgjI4CBmMSOkCBnpG1+urJ5MnNvVdkAQATmY4CACijowAAyugoAIAyOgoYjEnoAAV6RlYzt5pZZtasxtduNwMATCQ6CgCgjI4CACijowAAyugoYDAmoQMUGE5kOdMPAJjIdBQAQBkdBQBQRkcBAJTRUcBgTEIHKCCyAADK6CgAgDI6CgCgjI4CACijo4DBmIQOUKCjo/H1cG43I7IAgIlERwEAlNFRAABldBQAQBkdBQzGJHSAAq0802/27KSuhz8mAIDxQEcBAJTRUQAAZXQUAEAZHQUMxiR0gAI9I2vmzObf2zOy5s9P5s4d/pgAAMYDHQUAUEZHAQCU0VEAAGV0FDAYk9ABCgznTL/11uu9zC1nAICJQkcBAJTRUQAAZXQUAEAZHQUMxiR0gALDiayVVuq9/ezZwx8TAMB4oKMAAMroKACAMjoKAKCMjgIGYxI6QIHhRFbS+5YzzvQDACYKHQUAUEZHAQCU0VEAAGV0FDAYk9ABCogsAIAyOgoAoIyOAgAoo6MAAMroKGAwJqEDDNHChcmjjzYuG2pkzZrV+NrtZgCAiUBHAQCU0VEAAGV0FABAGR0FNGPCT0Kvqmrtqqo+U1XV9VVVPVpV1Zyqqn5TVdW7qqqa2oL9b1NV1ZFVVV1WVdVtVVXNq6pqQVVV91RV9aOqqg6uqmpKKz4LMDo6Onovc6YfMBHpKGCodBRAJx0FDJWOAuiko4Ch0lEAnXQUMFQ6CmjGhP4/96qqnpPke0lmJflJktOSTE9ycJJTkhxYVdVedV0/ULj/k5IctvRlR5Jzk9ycZOUkz07y2iQvT/KeqqpeXtf1vcP4OMAo6SuyZs4c2j5EFjDe6SighI4C0FFAGR0FoKOAMjoKQEcBZXQU0IwJOwm9qqqNk1yWZO0kx9d1/f5u605O8rMkuyT5XlVVu9V1vajgMGsv/fP6JC+s67rhP81VVb0syY+S7JDkm0l2LTgGMMoeeqjx9SqrJCusMLR9uN0MMJ7pKKCUjgImOh0FlNJRwESno4BSOgqY6HQUUEpHAc2YNNYDGEPHpTOC7kzyke4r6rqen+RtSep0htahwzzWu3oG1tLjXJ7k4qUvX1hV1bbDPA4wCnpG1lDP8kv6PtOvrsvHBDDKdBRQREcB6CigjI4C0FFAGR0FoKOAMjoKaMaEnIReVdVT0nmrlyQ5r67rBT23qev6H0l+vfTlUVVVVQWH+leS3yT57QDb/KHb820KjgGMsp6RtcYaQ99Hz8h6/PHk4YeLhwQwanQUMBw6CpjIdBQwHDoKmMh0FDAcOgqYyHQUMBw6CmjGhJyEns7AWhZNvxhgu58v/XPDJM8Z6kHquv5oXde71HX9xACbPdbt+fyhHgMYfa2IrPXW673snnvKxgMwynQUUExHAROcjgKK6ShggtNRQDEdBUxwOgoopqOAZkzUSei7dXv+pwG2+2O357uP0FietfTPBek8KxBoc62IrBVXTNZcs3HZ7NnlYwIYRToKKKajgAlORwHFdBQwwekooJiOAiY4HQUU01FAMybqJPSnL/3zkbqu5w6w3V3dnj+t1YOoqmqHJPsvffmZuq4fbPUxgNZrRWQlvW8540w/YJzQUUAxHQVMcDoKKKajgAlORwHFdBQwwekooJiOApoxZawHMNqqqloxybIbPdw3yObd12/SgmOvlmSVJBsneUWS9yZZlOSwuq6/WrjPDQbZpI+bWgDD0arImjUr+dvfnnwtsoB2p6OA4dJRwESlo4Dh0lHARKWjgOHSUcBEpaOA4dJRQDMm3CT0JDO6PX98kG3n9/O+Ut9Psmu31z9KckRd1zcNY593Db4J0EodHY2vW3Wmn9vNAOOAjgKGRUcBE5iOAoZFRwETmI4ChkVHAROYjgKGRUcBzZiIk9CndXu+cJBtu6+f3oJjvz/JmknWSPLcJAcm+UdVVd9Jcnhd14OdeQi0AbebASYwHQUMi44CJjAdBQyLjgImMB0FDIuOAiYwHQUMi44CmjERJ6F3P3tvhUG27b5+3nAPXNf1dd1efqOqquOS/DzJ65LsWFXVznVd3z/E3W44yPr1klw7xH0CA+gZWTNnlu1HZAHjkI4ChkVHAROYjgKGRUcBE5iOAoZFRwETmI4ChkVHAc2YiJPQH+n2fKVBtu1+VuAj/W5VqK7ru6uqOjDJ75JsmuSEJPsPdR8Dra+qqnyAQJ9adabfrFmNr91uBhgHdBQwLDoKmMB0FDAsOgqYwHQUMCw6CpjAdBQwLDoKaMaksR7AaKvrekGSe5e+XHeQzbuvv2OExvP7JLcsffm6qqpWHonjAK2xZEnS0dG4rJW3m6nrsn0BjAYdBQyHjgImMh0FDIeOAiYyHQUMh44CJjIdBQyHjgKaNeEmoS/196V/zqiqarUBttugj/eMhJuW/jk1yVNH8DjAMM2d2zuEWhVZCxb0DjiANqSjgCI6CkBHAWV0FICOAsroKAAdBZTRUUCzJuok9Cu7Pd9hgO2e2e35FUM5QFVVa1dV9dqqqjZpYvMnuj2fMpTjAKOr561mkvLIWm+93svuuadsXwCjSEcBRXQUgI4CyugoAB0FlNFRADoKKKOjgGZN1Eno3+72fI8Btttz6Z93J/ndEI/xtCQXJ3ltE9tu2e35nUM8DjCKekbWCisk06eX7WuFFZK11mpcNnt22b4ARpGOAoroKAAdBZTRUQA6CiijowB0FFBGRwHNmpCT0Ou6vinJd5a+PKCqqhV6blNV1VZJnr/05bF13XiDiaqq1q+q6g9VVT1YVdXrBjjcKwYaS1VVO6bF6J1DAAEAAElEQVQzyJLkurqu723qQwBjomdkrbFGUlXl++t5yxln+gHtTkcBpXQUMNHpKKCUjgImOh0FlNJRwESno4BSOgpo1oSchL7UB5LMSbJJks90X1FV1bQkZySpkvx26fOeDk/yrCRrJvnKAMfZraqqD1dVNbnniqW3ovn60peLk3xwSJ8AGHUdHY2vS281s8ysWY2vRRYwTugoYMh0FEASHQUU0FEASXQUUEBHASTRUUABHQU0a8pYD2Cs1HV9e1VVeyf5XpIjq6raNsllSaYnOTjJNkn+kGSfuq4X9bGL7hP4+zrP5/4ks5PMSvL5JAdWVXVZkluXrt8xyRuXHu/hJG+t6/qK4X4uYGT1dabfcPQ808/tZoDxQEcBJXQUgI4CyugoAB0FlNFRADoKKKOjgGZN2EnoSVLX9W+rqtouyXuT7JPkuCQLk9yYzjP5/qefwEqSk5K8OMlGSd7Tx77/UVXVxkleluQ/03lW4FuSrJrkiSQPJflVkp8kOa+u6wdb9sGAEdMzsmbOHN7+3G4GGK90FDBUOgqgk44ChkpHAXTSUcBQ6SiATjoKGCodBTRrQk9CT5K6ru9P8pGlj6G87+4kzxxkm0XpPHvwsuIBAm2l1Wf6ud0MMJ7pKGAodBTAk3QUMBQ6CuBJOgoYCh0F8CQdBQyFjgKaNWnwTQBYxu1mAADK6CgAgDI6CgCgjI4CACijo4BmmYQOMAQjHVn33JPU9fD2CQDQjnQUAEAZHQUAUEZHAQCU0VFAs0xCBxiCkY6shQt7HwMAYHmgowAAyugoAIAyOgoAoIyOApplEjrAELQ6stZdt/eye+4Z3j4BANqRjgIAKKOjAADK6CgAgDI6CmiWSegAQ9DR0fh6uJG1wgrJ2ms3Lps9e3j7BABoRzoKAKCMjgIAKKOjAADK6CigWSahAzSprlt/pl/S+5YzzvQDAJY3OgoAoIyOAgAoo6MAAMroKGAoTEIHaNK8ecnChY3LWhFZs2Y1vhZZAMDyRkcBAJTRUQAAZXQUAEAZHQUMhUnoAE3qeZZfksycOfz99jzTz+1mAIDljY4CACijowAAyugoAIAyOgoYCpPQAZrUM7KqKlltteHv1+1mAIDlnY4CACijowAAyugoAIAyOgoYCpPQAZrUM7JmzkwmteC/om43AwAs73QUAEAZHQUAUEZHAQCU0VHAUJiEDtCknpG1xhqt2a/bzQAAyzsdBQBQRkcBAJTRUQAAZXQUMBQmoQM0abQi6557krpuzb4BANqBjgIAKKOjAADK6CgAgDI6ChgKk9ABmjRakbVoUTJnTmv2DQDQDnQUAEAZHQUAUEZHAQCU0VHAUJiEDtCkjo7G162KrHXXTaqqcdk997Rm3wAA7UBHAQCU0VEAAGV0FABAGR0FDIVJ6ABNGqkz/aZOTdZeu3HZ7Nmt2TcAQDvQUQAAZXQUAEAZHQUAUEZHAUNhEjpAk0YqspLet5xxph8AsDzRUQAAZXQUAEAZHQUAUEZHAUNhEjpAk3pG1syZrdv3rFmNr0UWALA80VEAAGV0FABAGR0FAFBGRwFDYRI6QJNG80w/t5sBAJYnOgoAoIyOAgAoo6MAAMroKGAoTEIHaJLbzQAAlNFRAABldBQAQBkdBQBQRkcBQ2ESOkCTRjKy3G4GAFie6SgAgDI6CgCgjI4CACijo4ChMAkdoAkLFyaPPda4zO1mAAAGp6MAAMroKACAMjoKAKCMjgKGyiR0gCZ0dPReNtKRtWRJ6/YPADBWdBQAQBkdBQBQRkcBAJTRUcBQmYQO0ISet5pJkpkzW7f/nrebWbQomTOndfsHABgrOgoAoIyOAgAoo6MAAMroKGCoTEIHaELPyJoxI5k6tXX7X3fdpKoal7nlDACwPNBRAABldBQAQBkdBQBQRkcBQ2USOkATekZWK8/ySzqDba21Gpfdd19rjwEAMBZ0FABAGR0FAFBGRwEAlNFRwFCZhA7QhJ6RtcYarT/Gmms2vu7oaP0xAABGm44CACijowAAyugoAIAyOgoYKpPQAZowGpHVc589jwkAMB7pKACAMjoKAKCMjgIAKKOjgKEyCR2gCaMRWT1vYSOyAIDlgY4CACijowAAyugoAIAyOgoYKpPQAZrgTD8AgDI6CgCgjI4CACijowAAyugoYKhMQgdoQkdH4+vRiKyexwQAGI90FABAGR0FAFBGRwEAlNFRwFCZhA7QBGf6AQCU0VEAAGV0FABAGR0FAFBGRwFDZRI6QBNGI7Jmzhz4mAAA45GOAgAoo6MAAMroKACAMjoKGCqT0AGa4Ew/AIAyOgoAoIyOAgAoo6MAAMroKGCoTEIHaELP4Ol5Vl4r9Iysjo7WHwMAYLTpKACAMjoKAKCMjgIAKKOjgKEyCR1gEIsXJw8/3LjMmX4AAIPTUQAAZXQUAEAZHQUAUEZHASVMQgcYxNy5SV03LhuJyOp59uD8+Z0PAIDxSkcBAJTRUQAAZXQUAEAZHQWUMAkdYBB9nXE3Gmf6JW45AwCMbzoKAKCMjgIAKKOjAADK6CighEnoAIPoGVkrrphMm9b646y+eu9lIgsAGM90FABAGR0FAFBGRwEAlNFRQAmT0AEG0TN01lgjqarWH2fKlGS11RqX9XWWIQDAeKGjAADK6CgAgDI6CgCgjI4CSpiEDjCInqEzEreaWWbmzIGPDQAwnugoAIAyOgoAoIyOAgAoo6OAEiahAwxiNCOr575FFgAwnukoAIAyOgoAoIyOAgAoo6OAEiahAwxiLCOr561uAADGEx0FAFBGRwEAlNFRAABldBRQwiR0gEH0jKyet4RpJWf6AQDLEx0FAFBGRwEAlNFRAABldBRQwiR0gEGM5pl+PQNOZAEA45mOAgAoo6MAAMroKACAMjoKKGESOsAgxvJ2MyILABjPdBQAQBkdBQBQRkcBAJTRUUAJk9ABBjGWkdXRMXLHAgAYaToKAKCMjgIAKKOjAADK6CighEnoAIPoGTrO9AMAaI6OAgAoo6MAAMroKACAMjoKKGESOsAgRvNMv5kzBz42AMB4oqMAAMroKACAMjoKAKCMjgJKmIQOMIC6HtvbzYgsAGC80lEAAGV0FABAGR0FAFBGRwGlTEIHGMBjjyWLFjUuG83IevjhZPHikTseAMBI0VEAAGV0FABAGR0FAFBGRwGlTEIHGEBfZ9r1vCVMK/UVcHPnjtzxAABGio4CACijowAAyugoAIAyOgooZRI6wAB6RtakScmqq47c8fqKLLecAQDGIx0FAFBGRwEAlNFRAABldBRQyiR0gAH0DJyZMztDa6RMm5asuOLAYwAAGA90FABAGR0FAFBGRwEAlNFRQCmT0AEG0DNw+joTr9V6HkNkAQDjkY4CACijowAAyugoAIAyOgooZRI6wADaIbI6Okb+mAAAraajAADK6CgAgDI6CgCgjI4CSpmEDjCAnoHjTD8AgOboKACAMjoKAKCMjgIAKKOjgFImoQMMYCzO9Js5c+AxAACMBzoKAKCMjgIAKKOjAADK6CiglEnoAANoh9vNiCwAYDzSUQAAZXQUAEAZHQUAUEZHAaVMQgcYQDtEVs9b3gAAjAc6CgCgjI4CACijowAAyugooJRJ6AAD6BlZPW8FMxKc6QcALA90FABAGR0FAFBGRwEAlNFRQCmT0AEGMBZn+vUMOZEFAIxHOgoAoIyOAgAoo6MAAMroKKCUSegAA2iH282ILABgPNJRAABldBQAQBkdBQBQRkcBpUxCBxhAO0RWR8fIHxMAoNV0FABAGR0FAFBGRwEAlNFRQCmT0AH6sWBBMm9e47KxOtOvrkf+uAAAraKjAADK6CgAgDI6CgCgjI4ChsMkdIB+9HWG3WhE1syZja8XLuwdewAA7UxHAQCU0VEAAGV0FABAGR0FDIdJ6AD96HmrmaR3AI2EvkKur7EAALQrHQUAUEZHAQCU0VEAAGV0FDAcJqED9KNn2Ky6ajJlysgfd7XVkqpqXNbXWYcAAO1KRwEAlNFRAABldBQAQBkdBQyHSegA/egZWaNxq5kkmTSp9xmFzvQDAMYTHQUAUEZHAQCU0VEAAGV0FDAcJqED9KNn2IzGrWb6O5bIAgDGEx0FAFBGRwEAlNFRAABldBQwHCahA/RjrM706+tYIgsAGE90FABAGR0FAFBGRwEAlNFRwHCYhA7Qj3aKrI6O0Ts2AMBw6SgAgDI6CgCgjI4CACijo4DhMAkdoB/tFFnO9AMAxhMdBQBQRkcBAJTRUQAAZXQUMBwmoQP0o+fZdaMZWTNnNr4WWQDAeKKjAADK6CgAgDI6CgCgjI4ChsMkdIB+ONMPAKCMjgIAKKOjAADK6CgAgDI6ChgOk9AB+tFOkdXzrEMAgHamowAAyugoAIAyOgoAoIyOAobDJHSAfrRTZDnTDwAYT3QUAEAZHQUAUEZHAQCU0VHAcJiEDtCPnmEzc+boHbvnsUQWADCe6CgAgDI6CgCgjI4CACijo4DhMAkdoA+LFycPP9y4zJl+AACD01EAAGV0FABAGR0FAFBGRwHDZRI6QB96BlYytpH1yCPJokWjd3wAgFI6CgCgjI4CACijowAAyugoYLhMQgfoQ19n1o1lZCV9hx8AQLvRUQAAZXQUAEAZHQUAUEZHAcNlEjpAHzo6Gl+vtFIybdroHX/mzN7L3HIGABgPdBQAQBkdBQBQRkcBAJTRUcBwmYQO0IeeQTOaZ/klyYorJtOnNy4TWQDAeKCjAADK6CgAgDI6CgCgjI4ChsskdIA+jHVk9XVMkQUAjAc6CgCgjI4CACijowAAyugoYLhMQgfoQztGVs9b4AAAtCMdBQBQRkcBAJTRUQAAZXQUMFwmoQP0oR0ia+bMxtfO9AMAxgMdBQBQRkcBAJTRUQAAZXQUMFwmoQP0oWfQ9Aye0eB2MwDAeKSjAADK6CgAgDI6CgCgjI4ChsskdIA+iCwAgDI6CgCgjI4CACijowAAyugoYLgm/CT0qqrWrqrqM1VVXV9V1aNVVc2pquo3VVW9q6qqqS3Y/05VVX2xqqrfLt33oqqqHqqq6ndVVX26qqr/aMXnAFprzpzG12uuOfpj6BlZHR2jPwaAgegooC86CmBwOgroi44CGJyOAvqiowAGp6OAvugoYLgm9CT0qqqek+QvST6a5O4kH0pybJLVk5yS5FdVVa1duO+tq6r6fZJrkhyZ5NEkX07yjiQnJ1k3yceS3FhV1f7D+iBAy7VDZPU8u9CZfkA70VFAf3QUwMB0FNAfHQUwMB0F9EdHAQxMRwH90VHAcE0Z6wGMlaqqNk5yWZK1kxxf1/X7u607OcnPkuyS5HtVVe1W1/WiIR5i+yTPXvr8gLquL+hx/GOXHn/3JOdVVfVQXdc/Lvs0QKu1Q2S53QzQrnQUMBAdBdA/HQUMREcB9E9HAQPRUQD901HAQHQUMFwT+Urox6UzsO5M8pHuK+q6np/kbUnqdIbWocM4zrd6BtbSY8xLcmCSRen8ezh+GMcAWkxkAQxIRwH90lEAA9JRQL90FMCAdBTQLx0FMCAdBfRLRwHDNSEnoVdV9ZQkr1368ry6rhf03Kau638k+fXSl0dVVVUVHu7S/lbUdX13Om9HkyRbVVW1ZeExgBZavDh5+OHGZe0QWR0doz8GgJ50FDAQHQXQPx0FDERHAfRPRwED0VEA/dNRwEB0FNAKE3ISejoDa1k0/WKA7X6+9M8NkzxniMe4OsneSX4wyHZ3dnu+0RCPAYyAhx9O6rpxWTtE1kMP9R4XwBjQUUC/dBTAgHQU0C8dBTAgHQX0S0cBDEhHAf3SUUArtPUk9KqqXlVV1a0jsOvduj3/0wDb/bHb892HcoC6ru+p6/oHdV3PHWTT1bo9f2woxwBGRs9bzSRjE1kzZza+Xrw4eeSR0R8HMD7pKGAs6ChgeaCjgLGgo4DlgY4CxoKOApYHOgoYCzoKaIW2noSeZJUkG4/Afp++9M9HBomgu7o9f9oIjCNJNl02liR/HqFjAEPQM7KmT09WWmn0x9HzTL+k82w/gCbpKGDU6ShgOaGjgFGno4DlhI4CRp2OApYTOgoYdToKaIUprd5hVVUfb+Hutm/hvpIkVVWtmGS9pS/vG2Tz7us3GYGxPCXJ1ktfnlPX9eOtPgYwdD0jayzO8kuSGTOSyZM7z/BbpqMj2WSTsRkPMPJ01JDGoqOgDekoYKzoqCGNRUdBG9JRwFjRUUMai46CNqSjgLGio4Y0Fh0FbUhHAa3Q8knoSY5JUo/AfltlRrfng0XN/H7e1ypvW/pnR5LPlOygqqoNBtlkvUHWAz20S2RVVefZfg888OQyZ/rBcu+Y6Khm6ShoQzoKGEPHREc1S0dBG9JRwBg6JjqqWToK2pCOAsbQMdFRzdJR0IZ0FNAKIzEJPUmqFu6r1cE2rdvzhYNs23399FYOoqqqrZIctvTlO+u6vr9wV3cNvgkwFD0jq6/bvoyWmTNFFkxAOmoQOgral44CxpiOGoSOgvalo4AxpqMGoaOgfekoYIzpqEHoKGhfOgpohUkjtN831XU9abiPJG8egbF1P3tvhUG27b5+XqsGUFXV9CQXJVkxyZfquv5mq/YNDF+7nOmX9A48kQUTgo4agI6C9qajgDGmowago6C96ShgjOmoAegoaG86ChhjOmoAOgram44CWmGkroTeKnVae9ZgkjzS7flKg2zb/azAR/rdagiqqpqc5PwkOyT5epIPDXOXGw6yfr0k1w7zGDChtHNkdXSMzTiAcUlHDU5HQYvpKGA5oaMGp6OgxXQUsJzQUYPTUdBiOgpYTuiowekoaDEdBbTCSExCPzjJb1q0r98kOahF+0qS1HW9oKqqe9MZH+sOsnn39XcM99hVVVVJzkjymiTfTnJgXddLhrPPuq7vHuSYw9k9TEjtHFnO9IPlno7qh46C8UFHAWNIR/VDR8H4oKOAMaSj+qGjYHzQUcAY0lH90FEwPugooBUmtXqHdV2fW9f17S3a3fOSnN2ifXX396V/zqiqarUBttugj/cUWRpYpyc5JMn3kryxrusnhrNPYGS0U2TNnNn4WmTB8k1H9U1Hwfiho4CxoqP6pqNg/NBRwFjRUX3TUTB+6ChgrOiovukoGD90FNAKLZ+EPk5c2e35DgNs98xuz68Y5jFPSvK2JJcmeYPAgvbVM2Sc6QfQQEcB/dJRAAPSUUC/dBTAgHQU0C8dBTAgHQX0S0cBrTCl1TusquqsFu5usxbuq7tvJ/nM0ud7JPllP9vtufTPu5P8rvRgVVWdkOTdSX6Y5HV1XS/qsX5WksuSnFHX9RmlxwFao53O9OsZWR0dYzMOYHToqN50FIwvOgoYKzqqNx0F44uOAsaKjupNR8H4oqOAsaKjetNRML7oKKAVWj4JPclBSeoW7atq4b661HV9U1VV30myb5IDqqr6TF3XCxsOXFVbJXn+0pfH1nVd91i/fjrP2tskyTvrur64r2NVVfXFJO9NcnmSfXseZ6kVkzwryfrFHwpomXaOLGf6wXLvoOio7tvpKBhndBQwhg6Kjuq+nY6CcUZHAWPooOio7tvpKBhndBQwhg6Kjuq+nY6CcUZHAa0wEpPQk2ROksdasJ+Vk4zUf94+kORF6YykzyT54LIVVVVNS3JGOiPvt0uf93R4OsMoSb6SpFdkVVX12SRHJrlz6TbPraqqr7GsV/YRgFabP7/z0d1YRtbMmY2vRRZMCDoqOgrGIx0FtAEdFR0F45GOAtqAjoqOgvFIRwFtQEdFR8F4pKOAVhmpSejvrev668PdSVVVb0pybgvG00td17dXVbV3ku8lObKqqm3TecuX6UkOTrJNkj8k2afn7WGWmtR9qD1XVlV1UJKPLH25UZIft270wEjpeZZf4kw/YNTpKB0F45KOAtqAjtJRMC7pKKAN6CgdBeOSjgLagI7SUTAu6SigVSYNvsmYqtNHwLRs53X92yTbJfl8ko2THJfko0n+nc4z+Z5X1/X9/bz9pCR/SudZje/pY/0mrR4vMPJ6RlZVJautNjZjSXpH1rx5yYIFYzMWYNzRUcCo0lHAckRHAaNKRwHLER0FjCodBSxHdBQwqnQU0CojcSX03ZLc0KJ9/Wzp/kbM0oj6SJ48K6/Z992d5JkDrD8myTHDGRsw+npG1syZyeTJYzOWpHdkJUlHR7Kem1TB8kpHRUfBeKWjgDGmo6KjYLzSUcAY01HRUTBe6ShgjOmo6CgYr3QU0Cotn4Re1/UvW7iv+5P0d6YdQMv1jKyxvNVMkqy+eu9lDz0ksmB5paOA8UxHAWNJRwHjmY4CxpKOAsYzHQWMJR0FjGc6CmiVSWM9AIB20m6RNXVqMmNG47KHHhqbsQAADERHAQCU0VEAAGV0FABAGR0FtIpJ6ADdtFtkJb1vOdPRMTbjAAAYiI4CACijowAAyugoAIAyOgpoFZPQAboZD5HlTD8AoB3pKACAMjoKAKCMjgIAKKOjgFYxCR2gm54B0w6RNXNm42uRBQC0Ix0FAFBGRwEAlNFRAABldBTQKiahA3TjTD8AgDI6CgCgjI4CACijowAAyugooFVMQgfoZjxEVkfH2IwDAGAgOgoAoIyOAgAoo6MAAMroKKBVTEIH6GY8RJYz/QCAdqSjAADK6CgAgDI6CgCgjI4CWsUkdIBuekZWz8AZCzNnNr4WWQBAO9JRAABldBQAQBkdBQBQRkcBrWISOsBSS5b0vpWLM/0AAAanowAAyugoAIAyOgoAoIyOAlrJJHSApR5+uDO0uhNZAACD01EAAGV0FABAGR0FAFBGRwGtZBI6wFI9bzWTtGdk9TwbEQBgrOkoAIAyOgoAoIyOAgAoo6OAVjIJHWCpnpG10krJ9OljM5buZs5sfN3R0fuMRACAsaSjAADK6CgAgDI6CgCgjI4CWskkdIClekZWO5zll/Q+06+uk7lzx2YsAAB90VEAAGV0FABAGR0FAFBGRwGtZBI6wFIPPdT4ul0jK+k9VgCAsaSjAADK6CgAgDI6CgCgjI4CWskkdICl2vVMv+nTkxVWaFzW0TE2YwEA6IuOAgAoo6MAAMroKACAMjoKaCWT0AGWatfIqqpk5szGZc70AwDaiY4CACijowAAyugoAIAyOgpoJZPQAZZq18hKet9yRmQBAO1ERwEAlNFRAABldBQAQBkdBbSSSegAS/WMrJ5hM5ZEFgDQznQUAEAZHQUAUEZHAQCU0VFAK5mEDrDUeDrTr6NjbMYBANAXHQUAUEZHAQCU0VEAAGV0FNBKJqEDLNXOkTVzZuNrZ/oBAO1ERwEAlNFRAABldBQAQBkdBbSSSegAS7VzZLndDADQznQUAEAZHQUAUEZHAQCU0VFAK5mEDrCUyAIAKKOjAADK6CgAgDI6CgCgjI4CWskkdIAkjz+ezJvXuKydI6ujY2zGAQDQk44CACijowAAyugoAIAyOgpoNZPQAdL3mXPtFFkzZza+dqYfANAudBQAQBkdBQBQRkcBAJTRUUCrmYQOkN63mqmq3mEzltxuBgBoVzoKAKCMjgIAKKOjAADK6Cig1UxCB0jvyFp99WTy5DEZSp9EFgDQrnQUAEAZHQUAUEZHAQCU0VFAq5mEDpDekdVOt5pJekfWggXJ/PljMxYAgO50FABAGR0FAFBGRwEAlNFRQKuZhA6Q3pHVM2rGWl/jcbYfANAOdBQAQBkdBQBQRkcBAJTRUUCrmYQOkPY/02+11XovE1kAQDvQUQAAZXQUAEAZHQUAUEZHAa1mEjpA2j+yJk9OVl+9cZnIAgDagY4CACijowAAyugoAIAyOgpoNZPQAdL+kZX0vuVMR8fYjAMAoDsdBQBQRkcBAJTRUQAAZXQU0GomoQNkfEaWM/0AgHagowAAyugoAIAyOgoAoIyOAlrNJHSAjI/Imjmz8bXIAgDagY4CACijowAAyugoAIAyOgpoNZPQATI+IsuZfgBAO9JRAABldBQAQBkdBQBQRkcBrWYSOkB6B8t4iKyOjrEZBwBAdzoKAKCMjgIAKKOjAADK6Cig1UxCBya8uh6fkeVMPwBgrOkoAIAyOgoAoIyOAgAoo6OAkWASOjDhzZ2bLF7cuKwdI2vmzMbXIgsAGGs6CgCgjI4CACijowAAyugoYCSYhA5MeHPm9F7W86y6duBMPwCg3egoAIAyOgoAoIyOAgAoo6OAkWASOjDh9YysFVZIVl55bMYykJ6R1dExNuMAAFim5R31618nr3998t73JnfdNZyhNdBRAEC78X0UAEAZHQUAUEZHASPBJHRgwusZWWuumVTVMHda18mSJcPcSSNn+gEA7aalHfWHPyS7755cfHHyla8kW2+dHH988sQTwx6njgIA2k1LO2rJkuTEE5Nttkn23DO58cZhj28ZHQUAtJsR+b3eCNBRAEC70VHASJgy1gMAGGt9RdawHHts8oUvJI89lmy0UbLJJsnGGzf+uckmyfrrJ1Oa/8/wzJmNr+fO7ZyTNYRdAAC0VMs66uGHO6+AvnDhk8seeyx5//uT889P/ud/kmc/u3SYOgoAaDst66iHHkoOPDD5wQ86X99wQ7Lrrskvf5lstdWwxpjoKACg/bT893ojREcBAO1GRwEjwf80gQmvpZH1058mRx315Ot//avz0ZfJk5MNN2ycmL7xxp1XrXr2s3udbtjzTL+kc77WWmsNY7wAAMPQko6q6+SQQ5Lbbut7/Z//nOy8c/KOdySf+1yy+upDPoSOAgDaTUs66g9/SF772uSOOxqX339/ssceydVXJ5tvXjzGREcBAO1nvEye0lEAQLvRUcBImDTWAwAYay2LrLpOPvKR5rdfvDi5/fbOK1Ode27yyU92TsDaeedk770713fT80y/JOnoKBwrAEALtKSjTjop+d73Bt6mrpPTTku23jr5xjc6Xw+BjgIA2s2wOqquk1NPTXbZpfcE9GXuuSfZfffkzjuLx5joKACg/YyXyVM6CgBoNzoKGAkmoQMTXssi63vfS667btjjSZL88IfJ6ac3LJo2rfPR3UMPteZwAAAlht1R116bfOADvXfyne8kT3967+3vvTd54xuTl740+ec/mz6MjgIA2k1xRz36aLLffsm7350sXDjwtnfe2TkR/Z57isaY6CgAoP302VHLLmCw//7JkUcml1025tGiowCAdtPv91E//nHnRTd/+ctRH1NfdBSMLyahAxNez1ApmoS+eHHysY81LnvKUzqv1Hnssck735m8/OWdV++cPr25fX70o8l99zUs6nm2n8gCAMbSsDqqoyN5/euTRYsal59/fvKa1yR//GPyhS/0/pYpSX72s85J6p/+dLJgQVOH01EAQDsp6qi//z3ZaafO75t6ev7zk+uvT3bYoXH5v/6V7LFHcv/9pUPVUQBAW+mzoz73ueRd70q+/vXkS19KXvnKzhXbbtt58t43vpH83/+N+lh1FADQTvrsqM98JnnFK5LPfz550Ys6L37wwANjMbwGOgrGD5PQgQmvJVdCv/DC5IYbGpd9+tPJG96QfOhDnbdI/tGPkn/8o/OKVfffn1xzTXLxxclxxyWHHdYZdd3NnZt88IMNi9ZYo3ETkQUAjKXijqrr5JBDkttvb1z+4Q93nriXJFOndrbQP/6R7LVX730sWJB8/OPJ9tsnV1016CF1FADQTobcURdckDz72cmNN/Ze94EPJFdckTztaclPf9r5Z3c33pjsuWfvgzZJRwEA7aRn0jzliX8kn/xk3xtff33n7+je+MZkgw2SzTdPDjooOeus5JZbOr+jGkE6CgBoJz076qXX/7/k6KMbF150UecFNi+4YMRbaSA6CsYPk9CBCW/Yk9AXLkw+8YnGZTvskLz2tX1vX1XJ2mt3Xrnqta/t/EXhSSclP/xh520CuzvvvOR//7frpcgCANpJcUedeGJyySWNy3bZpfMkvp422SS59NLkO99J/uM/eq+/6aZkt92SAw8c8MoMOgoAaCdNd9Tjjydvf3tywAHJvHmN61ZbrbOpjjuu8wS+pPM7p5//vPMOfd397W/JS16SPPzwkMeqowCAdtK9oyZlcf7ze4f2vtNef269NTn33OQtb+nspfXX77xT30knJf/8Z8vHqqMAgHbSvaPemVPzoh98oP8NDzgg+c//TO68c3QG14OOgvHDJHRgwuv5S7+eITOor36191U8P/vZZFLBf2K/9KVk1VUbl73rXV1fnvUcW0fH0A8BANAqRR11zTXJkUc2Lltrrc7bIk+Z0vd7qip5zWs67zzz3vf23VnnnZc89anJD37Q5y50FADQTprqqFtvTZ73vOSMM3qve8Yzkj/+MXnVq3qvW2+95Be/SDbdtHH5H//YedeZRx4Z0lh1FADQThonT52Wdf7128YNnvKUZKWVmtvZvfd23rX4Pe/pfN9ZZ7VuoNFRAEB7WdZRB+acnJp3D/6GH/+48457J5+cLFkysoPrQUfB+GESOjDhDetK6PPm9b5i5y67dP5Cr8R66/Xe3/XXd14tNMnMmY2rnOkHAIylIXdUR0fn1aV6Xp3q/PM7b4k8mBkzkhNOSK69Ntlxx773v+++yc0391qlowCAdjJoR11ySfLMZyZ/+lPvN7/97clvfpNstln/B9hgg+SKK5INN2xc/rvfJXvt1fuq6gPQUQBAO1nWURvmznw+RzWu3Hzz5M9/TubO7eylY4/tvILnaqsNvuO6Tg4/vKVX+9RRAEA7mTMneUO+ka/lLb1XfvzjyWtf23v5o492NtILXtB5sahRoqNg/DAJHZjQFi7s7KXuhjQJ/eSTO6+S0N3nPtd5tc5S73pXssMOjcuOOSb5v/9zuxkAoG0MuaPqOjn44OSOOxqXH3VU8rKXDe3gz3xm5wSqk0/ufReZhQuTo4/u9RYdBQC0iwE7atGizrvGvPrVnZOnups+vfPkvdNPb+7qnpts0nlF9FmzGpdffXXnFdQff7yp8eooAKBdPNlRdU7LOzMjPaLqzDOTadOSFVZInvvc5EMf6rxr3pw5nZPTTzoped3rOi8K1Zd58zonWbWIjgIA2sXChckej16SC/KmTE6Pq5p/8pOdj4svTr773d7fJSWdJ/jtsEPnhTUXLhzx8eooGD9MQgcmtJ5XnUqGMAl97tzOKyh099KXJi984fAGNWVKcuqpjcsefTQ54giRBQC0jSF31Fe+knz/+43Lnv/85FOfKhvA5MnJu9/dedWF//zPxnXf+lbyxz82LNJRAEC76Lej5s9P9twz+dKXem+w1VbJNdckb3rT0A625ZadE9HXXrtx+c9/3nl1qyZ+aaijAIB2sayj3piL8p/5UePKQw9Ndtut7zdOnpxsv31y2GGd3xvdc09yyy3J176WvOhFjdteemnv77AK6SgAoF088p2f5Jt5Q6ZkceOKD32o8eJOr3518o9/dLZVTwsXdl4xfccdO+9aPIJ0FIwfJqEDE1pfv/TrGTL9+n//L+noaFz22c8Oe0xJOq/O8JYet7/51rey7X0/b1jU8/AAAKNlSB11zTXJBz/YuGyttZJvfKPzBLzhWH/9ziuCrr564/KPfGTAsekoAGCs9NtRn/pU51XKe3rjGzt/sfe0p5UdcOutk5/9rPd9jH/4w859P/HEgG/XUQBAu5gzJ1kzD+Yr+e/GFeutl3zxi83vqKqSLbZIDjmk82qf66zTuP7ww3vfuqaAjgIA2sJVV2XmwftkxTRejGDJuw9PPv/5zjbqbvXVO+8wc8UVyeab997f3/6W7Lxz8v73J489NiJD1lEwfpiEDkxoPX/pt9pqTc6Duv/+5PjjG5ftu2/yrGe1bGw59theVbXrxe/OClnQ9dqZfgDAWGm6ox56KHn965NFixqXX3BB8h//0ZrBzJzZeaWG7n7yk+Sqqxo26TksAICx0GdHLV6QfPWrjStWWKHzbnkXXpisssrwDrr99slPf5qsumrj8u9+N3nzm5PFi/t+X3QUANA+5sxJTsj7snYebFxxyim9o6VZM2f2/p3fXXclxxxTtr8eu+5ORwEAo+63v0322iuTFjzesPjcqYdm0olf7j0Bvbvddkv++tfkyCOTST2mmS5Z0tlQ227beRe+FtNRMH6YhA5MaD1/6bfmmk2+8dhjG8/mmzSp82pVrbTWWp1nHHaz6uyb84E8eUtmkQUAjJWmOqquk4MPTu64o3H5Rz6SvPSlrR3Qe97TedWr7o46qnMMcds+AKB99NlR3/9+8mCPyVRXXJG8850D/zJwKHbcMfnxj5OVV25cftFFnbdYXrKkz7fpKACgXUz5+eU5IBc0LnzNazofw7Hffsnuuzcu+/KXk7/8ZVi71VEAwJj64x+Tl7+819XKL8j++cwGp/eeWN6X6dM77zjz+98n223Xe/1ttyV77pmcc05rxryUjoLxwyR0YELrGSlNTUK/667Oq1B1d8AByTbbtGxcXQ49NHn2sxsWfTSfzca5PUnn+JfOqwIAGFVNddSXv5xcemnjshe8IPnkJ1s/oOnTk49/vHHZ736XXHZZkr6/rNJRAMBY6LOjel4F/UUvSnbZpfUHf97zkh/+MJk2rXH5Oeck731vn2/RUQBAW3j00Wx36tsbFj0yebXkpJOGv++q6vzd3worPLls8eLkHe/o90S9ZugoAGDMXH998pKXJHPnNiz+dvbNQTknM9eaPLT97bhj8oc/JJ/9bLLiir3XH354ct99wxhwIx0F44dJ6MCEVnQl9E9/Olmw4MnXU6cmn/hES8fVZdKk5LTTGs4+nJ75+Ur+O0nyxBO9TlgEABgVg3bU73+ffPCDjcvWXrvzSptTpozMoN7ylmSzzRqXfeQjyeLFvb6s0lEAwFjp2VHbTLst+dnPGhceeujIDWDXXZNLLmmcZJV0TuD6zW96ba6jAIC28LGPZcZDdzYs+tpWX0rWX781+3/qUzvvqtfd736XnHlm8S51FAAwJm6+ufPq5D2+hPphXpH98vUszpTm5kf1NHVq5+/d/vKX5PnPb1z36KPJMccUD7knHQXjh0nowITW85d+PSOml1tuSc46q3HZ296WbLppS8fV4JnP7Lz1cjevyqXZK51X9XTLGQBgLPx/9u47PIqqDePwM2kQekCqgIIoCiqo2HtvgCCIFduHoogde68oKFjA3hsWEAErFuxixYpio0vvPW2+P05C9uxsks323fnd17VXMu/OzhwMwsPMO+dUmaOWL5f69TNXhMo5jvTCC9KWW8ZvUHl55oHBQL/9Jr30kgoKvLuTowAAQDIE56iey5+2C40aSccfH99BHHGENHas9+HA4CwlkaMAAEDyTZ0qPfCAVZqig/Ttzv+L7XmuvlradltvLcJZPclRAAAg4WbOlA491JNf/mp7qPponIpkJiWotj+qKh07Sp98Ip1yil1/7DFp+vQoDlyBHAWkD5rQAfhajWdCv+kms/xeufx86brrYj4uj9tvl5o1s0oP6CLlaz0hCwAAJEWlOcp1pTPPlObYM1Pp2mtNs1O8nXSStPPOdu3GG9WgdmHg4jKSuFgFAACSIzBHZalEh8wMmvDgtNPMNad469FDuusuu/buu9K331qlBg1EjgIAAMlTWGhWiXHdzaUNqq1z9ZiabOHE9ly1a0sPPWTXVq6UhgyJ6HDkKAAAkFDz5pkG9Hnz7Pp++2nEgRO0SbU3lyKaCT1QVpY0bJh9Dau0VLriiigPbJCjgPRBEzoAX6tRE/pPP0ljxti1iy6SWraM+bg8GjWS7rnHKrXTLF2joYQsAACQFJXmqOeekyZNst884ICYLsFXpaws6c477dqsWcp64jHPrAnkKAAAkAyBOepIvadG6+bbOwwYkLjBnH++Z+ID3X67tZmV5Z19ihwFAAAS5q67zEp3AW7Wzfpb20bfPBXKYYd5Z/V84QXpww9rfChyFAAASJhFi0wD+syZdn333aW33tKC1XWtckxy1JZbeh/We/tt6YMPoj40OQpIHzShA/C1GjWh33CDvd2ggXTllTEfU6VOO800cAW4UsNUNP2vxI0BAACgTKU56tFH7TeaNjUP8uXkJGRckqRjjpH23deu3XabWjdaa5VWrEjckAAAAMoF5qgBesJ+s1s3qUuXxA2mTh3p8svt2sSJZjKGAMFLNJOjAABAQkyf7nlA7gftontl8ktcmtAl6d57pYYN7dr550sbN9b4UOQoAACQEP37S3/+add23tmsetegQc36o2riyiul5s3t2uWXSyUlUR+aHAWkB5rQAfha2CHrq6+8M3pecYU38cST40ijR6tY2ZtLtVSozg8PtpYgBAAASISQOWrOHJObAo0eLbVqlbBxSTK56a677NrixRq46X6rxIwJAAAgGcpzVHMtVA8FXW9K5Czo5c4/33uNK6jZi5mnAABAwpWWmmxUVLS5VKxsDdATKpGZ7CBuTegtWnivLf31l3T33TU+FDkKAADE3c8/S++/b9e2397Uyq75xK0JvV496bbbvON57rmoD02OAtIDTegAfC2skOW60rXX2rWmTaWLL47buCq14456Y+tLrVLr6ZOlceMSPxYAAOBrIXPU2LF2sVEj6bjjEjUk2377mRnRA5y+cJgaq2LgXKwCAADJUJ6jTtdzylVxxRt16kgnn5z4AdWvL11yiV0bN87MPFomuEedHAUAAOLuoYc8kx08XOdyTdOum7fj1oQuSeeeK+25p127807TjF4D5CgAABB3jz9ubzdvLn3wgdSs2eZS3JrQJenss6Udd7Rr110nrVsX1WHJUUB6oAkdgG+5rjeghAxZH34offyxXbv2WnODLgne3v0mzdOWdvGSS6Q1a5IyHgAA4D+V5qhXX7WLvXtLeXkJG5fHnXdam3WLV+sqVcxYxcUqAACQaBU5ytUAPWG/2a+f1KBBMoYlXXihfW7XtbIUN/0AAEBCzZkjXXONVXK32UbXbrrZqsW1CT0rS3r0USm7YoViFRZKgwbVaIVichQAAIir9eul55+3a+ecI21Z0VcUdn9UpLKzpXvusWsLFkj33hvVYclRQHqgCR2Ab61eLRUX2zVPyAo1C3rr1tJ558V1bFWp27yeLtVIuzh/vnTrrckZEAAA8J1QOarZhtnS11/bxX79EjeoULp08cwmeqEeVCvNlyStWJGMQQEAAD8rz1H76zNtp6BZNAcMSM6gJLOCzUUX2bUxYzbP9Bl8048cBQAA4sZ1pfPPl9autcrrRj6utSX5Vi2uTeiSubYUvDLyBx+YnBQmchQAAIirsWOlVasqth1H+t//rF3C6o+K1pFHSkccYdeGDTPN6BEiRwHpgSZ0AL4VvNSMFCJkTZggffutXbvpJql27biNqzoFBdJY9dVkHW6/cd990m+/JWVMAADAX0LlqKafjrULBQXSoYcmZkBVufVWKSdn82a+NupGmYf3mDEBAAAkWnmO8syCvv320j77JH5AgS65RKpXr2K7tFQaOlSSiXaByFEAACBuXn5ZevttuzZggBZ3Ptiza9yb0CXpllvMBFWBLr007C4ochQAAIirxx+3t484Qtp6a6sUVn9ULNxzj1lNpty6ddINN0R8OHIUkB5oQgfgW8HhJDfXvs+mkhLp+uvtnbbdVjrjjLiPrSrmST9HgzVKm5RX8UZxcY2XAAQAAIhEqBxVa8KrdvH4480bydahg2dW0f/pSW2rP7lYBQAAEm75cqmhVuoEvWa/MWCAmakqmZo0MdeWAj3/vDRrFssfAwCAxFi61Ls6S4sW0rBh1d/Xi5d69aQHH7RrixdL110X1sfJUQAAIG6mT5c+/9yunXOOZ7eE5aiddpLOPtuuPfWU9PPPER2OHAWkB5rQAfhW8JN+jRsH3esbM8Y7s/ittya9mao8ZP2l7TRMV9pvfvqp9MoriR8UAADwleAc1aXhLDnffGMX+/VL3ICqc8MNUn7Fcs05KtGtupGLVQAAIOGWLZNO0UvK18aKYm6u1L9/8gYV6PLLrdyk4mLprru46QcAABLjiitMI3qg0aOlgoLq7+vF03HHST162LVHHpG+/rraj5KjAABA3DwRtNJes2bezKIw+qNi6dZbpbp1K7Zd12S8CJCjgPRAEzoA3woOWdZSM4WF0k032TvsvHNKNFMFhqyhukZzs7e2d7j7bmZDBwAAcRWco07MCprJs0kT6WDvEslJ06qVZxatk/SKtlz0Q5IGBAAA/GrZMmmAgm4QHnecuUmYCpo1kwYOtGtPP61WpfOs0ooVCRwTAADwhyVLzCosgXr3NqvtqZr7evHmOGY29Dp1Kmqua3JTcXGVHw1uniJHAQCAmNi4UXruObt21llSXp5n14TmqJYtpSuDJtScPFl6990aH4ocBaQHmtAB+FaVIeu556R//7V3uOMOKSv5f2wGhqwNqqOrc++xd/jxR2nKlISOCQAA+Etwjjp2/at2oXfvpK8e43HVVSqu38gqXbwkvGWTAQAAYiXrxx+0q6bZxQEDkjOYylxxhVSrVsV2YaE6vzXM2oWZpwAAQMyNHSuVlFRs5+dLo0Zt3kxqE7okbbWVdPPNdu2nn6QHHqjyY8zgCQAA4mL8eG9AquQaU8Jz1OWXmwmiAg0ZUu3De8HIUUB6SH43JQAkSZUh68UX7Tf33ls69ti4jykcBQX29piNveVu08EujhiRuAEBAADfCcxR7fSvdlj7nb1DCqwe41FQoOX/s2deOKLkXRV98EmSBgQAAPxomyn2LOiL89tKhx2WpNFUolUr6X//s0rNJz2u5lq4eXvtWrOQIAAAQMy8/LK93bOn1byU9CZ0SbrkEmmnnezajTdKc+dW+pHg+3rkKAAAEBOPP25vH3yw1KFDyF0TnqPq1jUTfQb67Tfp6adrdBhyFJAeaEIH4FuVhqzly6XPPrPfvPJKs9ReCgh+0s9VltYMuNQuvvWW9PvviRsUAADwlcAcdYJes99s0sRc6EpB7oUXaYFa2LVrrjHLJwMAAMTb+vXa8Wd74oOvtj9bys5O0oCqcNVV1so2WZs2aojs1fhYAhkAAMTMvHnee3MnnWRtpkQTem6u9Mgjdm3dOumiiyr9SPB9PYkcBQAAovTXX9KUKXbt3HMr3T0pOap/f6lLF7t2ww3SmjVhH4IcBaQHmtAB+FalIeudd+zl/mrXlo44ImHjqk7wk36StOCIM7zp6777EjIeAADgP4E5qp9etd/s00fKyUnsgMJU0LqubtMNVi3vu6+kSZOSNCIAAOArY8cqv3D15s1SOZq+51lJHFAV2raVzjjDKp2vh7WFlmze5qYfAACImVdesScJaNhQOvpoa5eUaEKXpH328TZ5vfGG9OWXIXcPdV+PHAUAAKLyhL3Snpo0kXr3rnT3pOSo7Gzp3nvt2qJF0rBhYR+CHAWkB5rQAfhWpSEruAnpsMOkOnUSMqZw5OVJ9erZtWUb60rnnWcXn3tOWrJEAAAAsVaeo9rrH+2mH+w3+/VL/IDClJcnvVx3gP5Re/uN666zH0IEAACIh6AbhO/pSGW3a5ukwYThmmusWdrrar0u0X2bt5cvT8KYAABAZhozxt4+/nipVi2rlDJN6JI0dKjUtKldGz485K6h7uuRowAAQMQKC6VnnrFrp5/uyU6BkpajDj1UOvZYu3bvvWYVnDCQo4D0QBM6AN8KGbIKC81M6IF69kzYmMIV/LTf8uWSBg+2lknWxo3Sww8ndFwAAMAfynPUCXrNfqNpU+nAAxM/oBqo1zhPN+pWu/jrr9JLLyVnQAAAwB9mzJA++8wqPaEByW2eqk779tKpp1qlC/WgGslMOcVNPwAAEBN//SV9/71dO/lkz24p1YTeuLF5YC/QhAkm84UQ8r4eAABAJCZNkhYvtmvnnFPlR5Kao4YNsyY50IYN0vXXh/1xchSQ+mhCB+BbIUPWZ59Jq1fbb3TvnrAxhatxY3t7+XJJLVtKp5xivzF6tGlGBwAAiKHyHNVPr9pv9Okj5eQkfkA10LixNEYn6yftbL9x443mgUQAAIB4ePJJa3OxmmqSeqR2E7pkmqscZ/NmA63RRXpAEjf9AABAjLz8sr3drJl08MGe3VKqCV2SBgyQGjas2HZdM7NnCCHv6wEAAETiscfs7f32k3bYocqPJDVHderkbZJ/7jlp2rSwPk6OAlIfTegAfCs4mDRpImniRLu4++6muTvFBIesFSvKvrn0UvuNxYulF19MyJgAAIB/LF8uddBf2lVBF4j69UvOgGqgcWPJVZau0x32G7NmeZrDAAAAYqKwUHr2Wav0rM5QkfKS3zxVne2392S8S3Sf6mt1xfUoAACASLmuNGaMXTvhhJCTHIS8r5dM9etLgwbZtWeflRYu9Oxa6X09AACAmpg1S3r/fbtWzSzoUgrkqFtuMdmpnOtKl19uvlaDHAWkPprQAfhSUZF3wvPGBa63Cb1nz8QNqgYqfdKvSxfpsMPsN0eMCCu4AQAAhKM8R52g1+w3mjWTDjggOYOqgfIc9ZaO1efa135zxAiptDTxgwIAAJntzTc9yyQ/qf9J8l7jSUnXXWdtFmilLtBoZp4CAADR+/ln6fff7drJJ3t2C3lfLxVy1EUXSXl5FduFhdKDD3p2YwZPAAAQE08+aff/NGwo9e1b5UdSIkc1ayZdfbVdmzJFeuutaj9KjgJSH03oAHwpVChpvvQ389RgoB49EjKemioosLetX89ll9lvTp8uTZ4c9zEBAAB/KM8d/fSq/UafPlJ2duIHVEMVOcrRjbrVfvPvv02TGAAAQCw9/ri1+Zn20wxtLykFZvAMx047Sb17W6XLNEJrF61L0oAAAEDGePlle7ttW2nvvT27hbqvlxI5qkUL6fTT7dpDD0lr1lilKu/rAQAAhKO4WHrqKbvWv79Up06VH0uZHHXppVKbNnbtiitMl3wVyFFA6qMJHYAvLVvmrTX6LGgW9LZtpZ13TsyAaqjKJ/2OOkrq1Mne4d574z4mAADgD8uWSdtphrrqJ/uNfv2SM6AaCsxRU3SwZjfqYu8wcmRiBwQAADLbnDnSe+9ZpSc0YPP3KTGDZziuv97abKql2vWbR5I0GAAAkBFc19uEfuKJUpa3hSHUfb2UyVFDhkiOU7G9cqX0xBPWLszgCQAAovb229J//9m1c86p9mMpk6Py86U777Rrf/zhmbwhGDkKSH00oQPwpeCQVb++lPP2JLvYs6d90SiFBIesFSsCNhzHPEEY6P33pV9+ifu4AABA5lu2TDpBr9nF5s2l/fdPzoBqyM5Rjl5rG7SKzMcfS9OmJXBEAAAgoz39tLVM8io10FiZZZLr15fy8pI1sBradVf9vf2xVuno34ZLGzYkaUAAACDtTZ3qXaH45JND7hrqvl7K5KiOHaXjjrNrI0das3pWeV8PAAAgHMHN2nvsEdbEmimVo045RdptN7t2002eVWQCkaOA1EcTOgBfCg5ZHRsulL7+2i726JG4AdVQtU/6nXaa1LSpXRsxIq5jAgAA/rBsmdRPr9rFvn2l7OzkDKiGgnPUG7VOMksnB2I2dAAAEAslJZ5lkl/SKVqvupKStPRxFH7tdYO13bhwkWeWTwAAgLCNGWNvd+wode0actfg+3opl6OuuMLenjtXeuWVzZvM4AkAAKIyb56ZCT3QueeG9dGUylFZWdK999q1pUurvL5EjgJSH03oAHwpOGT1zH7LmpVK9etLBx6Y2EHVQEGBve0JWbVrSxdcYNdefFFasCCu4wIAAJmv+Nc/tLOCVljp1y85g4lAcI5avDJPGjzYLo4Z413SEAAAoKY++ECaM8cqPaEBm79PueapahTvtqcm63C7OGyYtGlTcgYEAADSV0mJ9GrQJAcnn1zpCsUp1TwVyj77SPvua9eGDdt877Ha+3oAAABVeeopqbS0YrtePenEE8P6aMrlqAMPlLp3t2sjRliryAQiRwGpjyZ0AL4UHLIOXT/JLhx5pFSrVuIGVENhPel3/vn2r6GoSHroobiOCwAAZL7mn75mbS+r1dJ7ky2FhcxRAweah/jKFRdLo0cndFwAACADBc3itKhVV/2gXTdvJ/2mXw01bizdJns2dM2bJz37bHIGBAAA0tfHH0uLFtm1k06qdPeUa54K5cor7e1ffpHee08SM3gCAIAolJRITz5p1045xTSihyElc9Q119jb8+Z5V8kpQ44CUh9N6AB8KTBk1dYG7bZ8sr1Dz56JHVANBYesFSvshx4lSc2aSaefbtceflhavz6uYwMAAJmtwzR7lqpvt+orZWcnaTQ1FzJHNd5COuMM+41HHiE3AQCAyC1eLE2YYJW+6jRAUsXsnilx068GGjeWPtf++lhBqwcOHVrpbFUAAAAhBTcZ7bKL1LFjpbunZPNUsO7dpe23t2vDh0sK874eAABAKO+/71lpT+eeG/bHUzJHVbOKTCByFJD6aEIH4EuBIetQfahaJRsqCllZ0jHHJH5QNRAcskpLpTVrQux4ySX29rJl0nPPxWtYAAAg002frpZLf7VKf+x4QpIGE5lKc1Rwblq+nNwEAAAi9/zzdmN27dr6qMUp1i4pcdOvBspzlGc29FmzpJdeSvh4AABAmtq0SRo3zq6dfHKVH0nJ5qlgWVnSkCF27aOPpO++C/++HgAAQLDHHrO3d9lF2m23sD+esjnqqqvs7d9+k955x7MbOQpIfTShA/ClwOVZemiS/ea++6ZQ6gqtoMBbC7nkTKdO0tFH27WRI3ksEAAAROa116zN/9RSq3bct5KdU1OlOWr77b0PIpKbAABAJFxXeuIJu3bCCZq3zg4iKX75yaM8R32kQ/Sl9rbfvOeekLNVAQAAeEyeLK1caddOPLHKjwTfA0vZHHXaaVKLFnZt+PDw7+sBAAAEWrhQmhTU03TOOTU6RMrmqGOPlXbYwa7dfbdnN3IUkPpoQgfgS+VP+jkq9Tah9+yZ+AHVUL16Uk6OXas0ZF1+ub3955/SW2/FZVwAACDDvfqqtfmaTlBBk/T6Z2WVOerSS+03/vwz5KwLAAAAVfryS+mPP+zagAGemaeCZ3JKdRU5ytEdus5+89dfTUMZAABAdcaMsbf33Vdq27bKj6RNjqpVy7va3tixqrf43/Dv6wEAAJR75hmpuLhiu04d6ZRTKt09lJTNUVlZ0hVX2LVPP5WmTrVKNeqPApAU6dUtEAeO4zR1HOd2x3F+dRxnreM4yxzH+dJxnEGO4+TG+FzNHMcZ5ziO6zjOrFgeG0DNlIesXfWDWmmB/WYaNKE7jjcYVhqyDjlE2nlnuzZiRFzGBcBfyFGAz/z2mzR9ulV6Vf1SZ8aEMFWZow49VNppJ/tNchOAOCBHARkueBb0bbeV9t8/dZc/DlNgjnpHR+t3bW/vcO+9iR8UAN8hRwFpbt06acIEu3byydV+LK1y1MCBpluqXGmpnJEjwr+vBwBxQo4C0kxpqfT443btxBOlhg1rdJiUzlGnniq1amXXhg+3NmvUHwUgKXzdhO44zp6SfpJ0naR5kq6SdJekRpJGS/rccZymMTrXiZJ+k3R8LI4HIDrlIaunJtpvbLedeaWB4JC1YkUlOzqOdNlldu3jj6UffojHsAD4BDkK8KGgWdDnaUt9pb1T62JVmCrNUY7jnQ39o4+kn35KyLgA+AM5CshwGzdKr71m1wYMkBwntW/6hak8R7nK0ggFXW96/33p558TPygAvkGOAjLApEnS+vUV29nZ0gknVPuxtMpRjRqZRvRATz2l9g2WWqVK7+sBQByQo4A0NGWK9O+/du2cc2p8mJTOUXl53vty48dLM2ZYpbD7owAkhW+b0B3H2UrSJEktJY1wXfco13VHu647XNJukr6QtIek8dE88Vf+dJ+klyXNlMSzOECSuW4VTehpMAt6uYICe7vKJ/1OPllq2dKuMasngAiRowAfcl1PE/prOkGuslLrYlWYqsxRp5wiNW9u7zByZNzHBMAfyFGAD3z0kZnhs1xWlnT66db1qHLpnqOeV39tqBfUo8D1JgBxQo4CMsTLL9vbhx4qNWtW5UfSMkddcomUk1OxvWGD/rdxtLULM3gCSBRyFJCmgmdB79xZ2muvGh0iLXLUuefas7u7rme1vRr1RwFION82oUsaLqmppDmSrg18w3XdDZLOleRK2lfSgCjO842kY8vOsbekNVEcC0AMrF0rFRVJbTRHXRU0q2WPHskZVARqtNxMXp40eLBde+UVad68mI8LgC+QowC/+e036Y8/rNKr6icpBS9WhaHKHFWrljRokL3DSy9JCxbEfVwAfIEcBWS6iUETHuy9t9SixebrUYHSPUdtUm19u8cF9g4vvST9919iBwXAL8hRQLpbuVJ65x27dtJJ1X4sLXNU69ZmooMAJyx6UPmqmAWe5ikACUSOAtLNkiXS66/btXPPNSv61kBa5KgGDaTzz7drzz4rLVy4ebNG/VEAEs6XTeiO42wnqW/Z5nOu624K3sd13ekyT/tJ0jWOU8M/xSvMkLSr67pDXdctifAYAGKo/Cm/Hppkv9G4sbTPPokfUIRqHLLOO0+qU6diu7hYevDBmI8LQGYjRwE+FTQL+hy10dfaU1IKXqwKQ7U56vzzTTN6uaIi6aGH4j4uAJmNHAX4gOtKk4KuN5Wtuhc865SUGTnqw46DpNq1KwpFRVxvAhBz5CggQ7z+ulRYWLGdlyf17l3tx9I2Rw0ZYm02LFqms/T05m2apwAkAjkKSFPPPWd3j9eqJZ12Wo0PkzY56qKLTDYsV1go3X//5k2a0IHU5ssmdJmAVR6aPqxivw/KvraRyjosau6ossAGIEVU2oR+7LH20ngpLjhkrVgRxgfOPNOuPfqoefQRAMJHjgL8xnU9Tehj1VeuspSTYyYoSDfV5qimTaX+/e3aww9LGzbEdVwAMh45Csh0P/zgnQW8kib0TMlR8zY1lU4/3S4+8gjXmwDEGjkKyARjxtjbxxwjNWpU7cfSNkfttJP5NQa4XPcqW8WSwrivBwCxQY4C0o3rSo8/btf69vVelAlD2uSoli2915ceflhavVpSBP1RABLKr03oBwd8P62K/X4I+P6QSE7kuq4byecAxM+yZVJ9rdYh+sh+o0eP5AwoQgUF9nZYT/pdcom9PM+qVdJTT8VyWAAyHzkK8JtffpFmzLBKr6qfJHPRJ+I5UZIorBx1ySX29rJl0vPPx2tIAPyBHAVkuuBZ0Dt0kDp2lOS96ZdROerSS+3iypXS008LAGKIHAWku0WLpI+C7sudfHJYH03rHHXFFdZme83U8XpdEjN4AkgYchSQbj77zHNfTueeG9Gh0ipHDRni7Wcqa8aPqD8KQML4tQl9x7Kva1zXXVXFfnMDvu8cx/EASKBly6QjNFl5Cli6JjdXOvLI5A0qAhEtN7Pttptn4NrsvvukElbDAhA2chTgN0GzoM9WW31dNhFKSi7ZF4awclTnzt58OHKkVFoat3EByHjkKCDTTZxob/fsufnmWfBNv4zKUdtv753cgetNAGKLHAWku9des6+p1K0rde8e1kfTOkcdeKC0++5W6UoNk+TSPAUgUchRQLoJngW9Y0dp//0jOlRa5aiOHaVevezayJFSYWFk/VEAEsZ3TeiO49SS1KJsc1E1uwe+v3VcBhQlx3FaV/VSxa8VQJnly6UeCpqZ6qCDUnTNmcpFHLIuu8zenjlTmjAhJmMCkNnIUYAPua6nCf01naDy1TtT+mJVFcLOUcG56Y8/pPfei8uYAGQ2chTgA/PmSdOCJpULaMwOzhsZl6Muv9x+499/pTfeSMSQAGQ4chSQIcaMsbePO06qUyesj6Z1jnIc6corrVI3fa+DNYXmKQBxR44C0tDy5ebhvUADBkQ8fXna5aig3KT586WXXqIJHUhxvmtCl1Q/4PuN1ey7oZLPpZK51by+Td7QgNS0fEmJjtVbdjF4dvA0EByyVqwI84P77y9162bX7r03JmMCkPHIUYDf/PST9NdfVulV9dv8fXAeSRdh56jDDzczogcaMSIuYwKQ8chRQKabFDThQUGBtO++mzdDLX+cjirNUQccIO22m/0m15sAxAY5Ckh3s2dLX35p104+OeyPp32O6t1b2mYbq3SFhod/Xw8AIkeOAtLNuHHSpk0V27m50hlnRHy4tMtRe+1lrjEFGjZMjRvZqxSTo4DU4scm9PyA7wur2Tfw/fAexQaQ8hr89pW2UFDSCl4yOA0UFNjbYT/p5zjeWT2//FKaOjUm4wKQ0chRgN8EzYK+rP5W+lYVSwin/IwJlQg7RzmOdOmldu2DD6RffonLuABkNHIUkOkmTrS3jz7a3Cgsk1bLH1eh0hzlON7Z0L/6yrwAIDrkKCDdvfKKvV1QIB1xRNgfT/sclZ3tyUlH6121WvpzkgYEwEfIUUC6GTvW3u7ZU2raNOLDpWWOCp4N/fff1W66PdEoM6EDqcWPTeiBT+/lVbNv4Pvr4zCWWGhTzWv3yj8K+NM2v9k3Bf9rurO01VZJGk3kgp9Q3LDBvMLSt6/UurVdY1ZPANUjRwF+4rqeJf++at1PUsWSf2lxsSqEGuWoU0/1XuAbOTIu4wKQ0chRQCZbu1b66CO7FrTqXlre9AuhyhzVt6/Upo29A7OhA4geOQpId2PG2Nt9+kh51f3vXCEjctSZZ6q4sX196cJNw8O/rwcAkSFHAelk+XLv9aV+/ULvG6a0zFHHHCPtuKNVavvKMGu7Rv1RAOLOj03oawK+r13NvoFPBa6pdK8kcl13XlUvSQuTPUYg1XSZYzehz9yxZyV7prZQy+SEveRMbq508cV2bdw46e+/ox4XgIxGjgL85McfPdlgciP7YldaXKwKoUY5qnZtadAgu/bii9KiRTEfF4CMRo4CMtnkyVJhwKRxOTnSUUdZu6TlTb8QqsxRoa43jR8v/fNP3McFIKORo4B09scf5hpToJNPrtEhMiJH5edr0zmDrdJJelmrf52TpAEB8AlyFJBOJk6UiosrtmvXNg3ZUUjLHOU40hVXWKU633+uvfWlVQu7PwpA3PmuCd113U2qCB7Nq9k98P3Z8RkRgIT680+1WTfDKi3eOz2b0Bs18tZqFLIGDJDq16/YLi2Vhg2rfH8AvkeOAnzm1Vft7a231tSi3axSWlysCqHGOer88+0ZugoLpYceivWwAGQwchSQ4SZNsrcPPFBq2NAqpeVNvxCqzVGhrjfdd1+cRwUgk5GjgDQXPAt6y5YmK9VApuSoWpdeoHWqs3k7V8XKHnVf8gYEIOORo4A0M26cvX3UUVK9elEdMm1z1EknSa1bW6UrZfcz0YQOpA7fNaGX+a3sa33HcRpWsV/gn2a/VboXgPQRdFNwgVrI3WW3SnZObTk5UoMGdm358hocoFEj01AV6Nlnpf/+i3ZoADIbOQrwA9f1NqH366dlyx2rlDYXq4LUOEc1by6ddppde/hh1voDUFPkKCATlZRIb75p13p6JzxI25t+QarNUQ0bSuecY+/w1FM1vGgFAB7kKCAdua708st2rV8/KTu7RofJmBzVvImez/ufVSt47TE6qADEGzkKSAerV5uV9gL16RP1YdM2R+XlSZddZpV6aYI66o/N21xqAlKHX5vQpwR837WK/XYN+P6j+AwFQEIFNaFPUg81aZq+fxQGL4Fc45B1ySVSrVoV24WF0ogR0Q4LQGYjRwF+8Ntv0r//2rUTTkjfi1Uh1DhHXXKJvb1kifTii7EcEoDMR44CMtHXX0tLl9q1Hj08u/kqR118sd1ctn699OijcR8XgIxGjgLS0bRp0p9/2rWTTqrxYTIpR73Q9DIVqyInZW9Yx2p7AOKNHAWkgzffNP065XJzQ15fqqm0zlEDBniW5LtCwzd/TxM6kDrSt/MyOmMDvj+0iv0OK/s6T9LU+A0HQEIsWyb388+t0iT1SK+QFSTqJvSWLaUzz7Rrjz5KWgNQFXIU4AdBD+6pTRsVd9lNq1bZZV/lqJ12kg4/3K6NHGlm9QKA8JCjgEw0caK9veOOUrt2Vqm4WP7KUW3bSiecYNcefNC+mQoANUOOAtLRmDH2drt20p571ugQmZajNjTfWq8pKCeNHCmtW5ecAQHwA3IUkA7GjrW3Dz/crDYXhbTPUfXrS4MGWaX+el4t9Z8k2pqAVOLLJnTXdWdIGle22d9xnLzgfRzH2V7SfmWbd7mu3VngOE4rx3G+cxxnqeM4JwR/HkAKeucdOSUlmzfXK18f6tD0CllBgm/6RbRi3xVXSFkBfx2sXSuNHh3VuABkLnIU4BPBTeg9emj5Csezm+9y1KWX2tvTp3uXRwSASpCjgAwV3ITes6dnl1A3xTI+R11+ub29YIG3EQ0AwkSOAtJQaan08st27aSTJMd7fakqmZij7tZVdnHZMlaNARA35CggDaxdK73zjl3r2zfqw2ZEjrroIqlWrc2beSrSxbpfUoT9UQDiwpdN6GWGSFomaWtJtwe+4ThOvqTHJDmSvir7PtiFknaT1EQq+9MNQGoLaqZ6X4drg+p4bpylk4ICezuiJ/222UY68US7dv/9zLoAoCrkKCCTLV4sTQ2a6KRHD8+SfZK3ASmdRJSjjjxS2mEHuzZyZMzGBMAXyFFAJvn7b+n33+1aiKWSfZmjunWTDjjArt17L6vIAIgGOQpIJ19+Kc2bZ9dOPrnGh8nEHPWTumqSuttvDB8ubdyYnEEB8ANyFJDK3nnHzgHZ2SEnOaipjMhRzZtLZ55plc7TI2qgVcyEDqSQnGQPIFlc153lOE4PSeMlXeE4zk6SJkmqI+ksSZ0kfSepl+u6RSEOEdjAX+kj247jtJe0T0CpbvlXx3FOC6h/6bruvzX/lQAIS2Gh58nBSeqhunWth+bSTrXLH4fr6qvt2aiWLZOeeEK6+OKIxwYgc5GjgAz31lt2c1C9etLBB2v5d/ZuvsxRWVnSJZdIAwdW1N57T/rtN6lz51gOD0CGIkcBGSZ49ZhmzaQ99vDsFpwzfJOjhgyRPv20YvuXX6QPPjBLSgNADZGjgDQTvAJKp07SjjvW+DCZmqPu0HXqoTcr3li4UHr6aen885MzMAAZjRwFpLhx4+ztQw6JyZTlGZOjLr9ceuyxzfcuG2q1BupRLV9+ZZIHBqCcn2dCl+u6X0naWdJQSVtJGi7pOkmrZZ7k28d13cWVfPxBSdNknha8qIrTHCDp+YDXFmX1LYLqB4T8NIDY+PRTac0aq/SmuqffUjNBYtaEvvPO0rHH2rV77jHN+wAQAjkKyGDBzVRHHCHVquWZMcG3Oap/f+8vntnQAdQAOQrIIMG5qXt389BaEN/mqGOPlTp2tGv33huXMQHwB3IUkCaKi6XXXrNrJ58sOZX2LVYqU3PU19pLH+hQ+8277pKKQvV+AkD0yFFAitqwQXrzTbvWp09MDp0xOWrbbT3/TS7RfVq9ZFOSBgQgmK+b0CXJdd3Frute67puJ9d167quW+C67t6u646q5Am/8s/Nc113V9d1t3Bd97Uq9nvGdV0njNczcfkFAjAmTrQ2v9YeWqQW6RuyygTf9FuxIoqDXXONvT1vnvTii1EcEECmI0cBGWjjRjOzd6AePSRl0MWqMhHnqPx876xUzz0nzZ0bk3EB8AdyFJABVqywZ/mWKl0q2bc5KitLuvRSu/bee9Kvv8ZlXAD8gRwFpIGPPpKWLLFrJ50U0aEyOUfdruvtN+fMkV54IbEDAuAr5CggBU2eLK1bV7GdlSX16hWTQ2dUjrrSnvW8lRZo1+nkJiBV+L4JHYAPuK6nCX2izE3BtA5ZkgoK7O2IZ0KXpH33lfbbz67dfbdUWhrFQQEAQFqZMkVav75i23GkY46RlGEXqxRljrrgAikvr2K7qMjMVgUAAPzj3XelkpKK7dq1pcMOC7mrr3PU6adLW2xh10aMiPmYAABAChk71t7u1k3q0CGiQ2VyjvpEB+rHevvaOwwdamdMAACQ2YJz0wEHSM2bx+TQGZWjdt9d/3U8yCod/+9w+pmAFEETOoDM9+uv0uzZVilTmtDDXv44XMGzoc+YIb3xRpQHBQAAaWPSJHt7772lZs0kZdjFKkWZo1q0kM45x6498YRZSQYAAPhD0IQHOvRQqW7dkLv6Okfl50uDBtm1F16QFiyI+bgAAEAKKC6Wxo+3a/36RXy4zM5Rjh6od529w19/Sa9VOskwAADIJJs2ee/L9ekTs8NnWo6aecJV1vbWG2dIEyYkaTQAAtGEDiDzBd0UnKWt9Kt2lJT+ISvmTehHHy116WLXhg41s8kDAIDM5rrei109emz+NtMuVkWdo66+2p4NvbBQGjYs6nEBAIA0UFQkvfOOXevZs9LdfZ+jLrhAqlWrYruoSBo1KubjAgAAKeCzz6SlS+1aFM1UmZ6jxm84Stp1V7t4xx3M6gkAgB98+KG0apVdO/74mB0+03JU8aFH6mftZBdvvZXcBKQAmtABZL6gZiozC7ojKf1DVvDFqlWrolylz3FMQ1Wg774z4RcAAGS2H3/0zuTtoyb0Gueo1q2ls8+2a489Jv33X9RjAwAAKe6zz7w3Cbt3r3R33+eoZs2k/v3t2sMPS+vWxXxsAAAgycaOtbd32UVq3z7iw2V8jlrtqOTa6+3ir796J4oAAACZZ9w4e3uffaRWrWJ2+IzLUU0c3aWgfqYff5TeeCMZwwEQgCZ0AJlt4ULp66+t0iRVNFOlfcgKuljlut57oDXWt6+0zTZ2bejQKA8KAABSXvDNrfbtpU6dNm9m3MWqWOSoa66RcnMrtjdtYjZ0AAD8IGjVPXXrVuVNQnKUpMsus7dXrJCeeSaWwwIAAMlWWiq9/rpd69s3qkP6IkcdeJzUubP9xu23s0oxAACZrKjI2zwdZW4Klok56hWdqN+1vf3GTTcxGzqQZDShA8hsb75pba7Nqq9PdODm7XQPWQUF3lq1SyBXJydHuvJKu/bRR9I330R5YAAAkNKCm9B79DCrpJTJtItVMclRbdtKZ51l1x59VFqwIOJxAQCAFOe63ib0nj2r/Ag5StIOO0jHHGPXRo6Mckk/AACQUr780kwOFahPn6gO6YsctTJLuvZau/jdd9LkyYkZFAAASLyPP/ZeTDn++JieIhNzVKmydbNutt/49VfvajwAEoomdACZLaiZ6qO8o1WkvM3b6R6y8vOlWrXsWtRN6JJ0+ulSixZ2jdnQAQDIXP/9Z25uBerRw9rMtItVMctR11xjHuIrt3GjNHx4VGMDAAApbPp0aeZMuxaUm4KRo8pcfrm9/c8/3oZ+AACQvoKbf3bcUerYMapD+iZH9esndehgv3HHHQkbFwAASLBx4+zt3XeXttoqpqfI1Bz1mk7QL9rRfvPmm5noAEgimtABZK7166X337dK44vtm4LBy96lG8fx/hpi0oReu7Z3meQ33pB+/z0GBwcAACknaPUYNWgg7b//5k3X9WYMclSZrbeWzjzTrj3yiLRoUYQjAwAAKS149Zg2baQuXSrdnRwV4OCDpa5d7dqwYeY/EgAASG+lpd5mqr59ozqkr3JUTo509dX2G599Jn36acLGBgAAEqSkRBo/3q5FuXpMsEzOUa6ydJNusd/8/Xfp5ZeTMzAANKEDyGCTJ0sbNmzedLOzNbH4aGuXdH/ST/IGxRUrYnTg886TGjWya3ffHaODAwCAlBLcTHX00VJexeox69dLmzbZu5CjAlx7rT0b+oYNzIYOAECmCp65u0cPcxesEuSoAI7jnQ196lTp7bdjNi4AAJAk334rzZtn16JspvJdjurf3zzgGOj22xMyJgAAkECffy4tXmzXYtyEnuk56g310jR1td+85RapuDjhYwJAEzqATBb05OCmPQ7QctmpKhNCVkGBvR2TmdAlqX59afBgu/bii9KcOTE6AQAASAnr10sffGDXetirxwQv2SeRoyzt2kmnn27XHnrIexERAACkt8WLTdN0oJ49q/wIOSrIiSealWQCXXedmT0VAACkr7Fj7e2OHaXOnaM6pO9yVF6edNVV9pvvvy99801CxgUAABIkODd16SJ16BDTU2R6jgo5G/pff0kvvJD4QQGgCR1Ahiou9szouWT/3tZ2VpbUsGEiBxUfES1/HK6LLpLy8yu2i4ule+6J4QkAAEDSffCBtHFjxXZ2tpkJPUDwxSpyVAjXXWf+25XbsIHcBABApnnrLbOecbl69aSDDqryI+SoILm5ZmaqQD/9JL36akzGBQAAksB1vc1UfftWuVpMOHyZo84+W2re3N7hjjviPiYAAJAgpaXS66/btb59Y34aP+SoSeqh/1p1s3e49VapqCixgwJAEzqADPXZZ551gGd1Oc7abtzYBK10F9cm9KZNpQED7NoTT0hLlsTwJAAAIKmCHtzTfvt5AkbwxSpyVAjt25tlkwONHk1uAgAgk0ycaG8feaRUq1aVHyFHhXDqqVKnTnbthhu4SQgAQLqaNk2aNcuu9ekT9WF9maPy86UhQ+wdJk6Ufv457uMCAAAJMHWq9N9/di0GuSmYP3KUo0m732rvMHOm9OyziRwSANGEDiBTjR9vb++2m/7LbmuVMmGpGcn0iQf6998Yn2DIECknp2J7wwbp/vtjfBIAAJAUpaXSm2/atR49PLsFX6wiR1UieDb09eulESOiPCgAAEgJGzdKkyfbtZ49q/0YOSqE7Gzp9tvt2t9/S888E+2wAABAMgTPgt6+vdS1a9SH9W2OOu88b6f6nXfGdUwAACBBxo2ztzt1knbYIean8UuOerv0KGmvvezibbdJhYWJGxQAmtABZCDXld54w6716pWxIWunnezt77+P8QnatjUzVAUaPVpavTrGJwIAAAn33XfSwoV2zUdN6DHPUR06eHPTqFHe/4AAACD9fPSRecCsXFaWdMwx1X6MHFWJXr2k3Xe3a7fcYiY/AAAA6cN1vU3offtKjhP1oX2bo+rVky65xK69+qo0Y0Y8hwUAAOLNdb1N6HGYBV3yUY76wTFN54HmzJGefDJxgwJAEzqADPTDD9LcuXYtg5vQd9vN3p4/X1q0KMYnueoq+4LhypXSo4/G+CQAACDhJk2yt7fbzryCkKNq4Lrr7DUN165lNnQAADLBxIn29j77SFtsUe3HyFGVcBzvjJ7z50sPPRT12AAAQAL9+qv01192LUbNVL7OURdeKDVoULHtutLQoXEfGwAAiKPvv5dmz7ZrffvG5VS+ylE7Hirtv7/9xh13mFUNASQETegAMk/wLOgdOkidO2dsyOrYUapb167FfDb0HXYwM1QFGjGC0AYAQLoLbkLv2TPkbuSoGthuO+nkk+3agw9Ky5dHeWAAAJA0ruvNTSFWjwmFHFWFww6TDjnErg0dyup7AACkk+BZ0Nu08a52EiFf56hGjaTBg+3aCy9Is2bFcWQAACCugnPTttt6p/aOEV/lqB8c6dZb7eL8+dLjjyduYIDP0YQOIPMEN6H36iU5TsaGrOxsqWtXuxbzJnRJuvpqe3vhQunZZ+NwIgAAkBBz5kg//WTXKmmmIkfV0PXX26vIrFkjjRwZgwMDAICk+OEH6b//7FolD+8FI0dVI3g29GXLWEUGAIB0Mm6cvd23r31NJAq+z1GXXCLVqVOxXVIi3X13HEcGAADixnW9ualPn5jlpmC+y1EHHSQdfLD9xp13SuvXJ2hkgL/RhA4gs/z9t1n6L1Dv3pK8k082bpygMSVA8JIzcWlC32MP7+xUw4ZJxcVxOBkAAIi74Nk8CwqkffYJuSs5qoa231466SS79sAD0ooVMTg4AABIuODctO22ZuqlMJCjqrHnntJxx9m1e++Vli6NeFwAACBBfv9d+u03u9anT8wO7/sc1bSpNHCgXXvqKTOzJwAASC8//2z6mQL17Ru30/kyRwXPhr5wofTIIwkZE+B3NKEDyCzBs6A3b25uZilzn/STpG7d7O24NKFL0jXX2Nv//iu99lqcTgYAAOIquJnq2GOlnJyQu5KjInDDDfYMFqtXS/fdF6ODAwCAhJo40d7u0SPsmarIUWG4/Xb7v+fatdLQoRGPCwAAJEjwbJ4tW0p77x2zw5OjJA0ZIuXlVWwXFpoH9gAAQHoJzk1bby3tumvcTufLHLXfftIRR9hv3nWXtG5dQsYF+BlN6AAyy/jx9nbPnmY9FmV2yAp+0m/ePGnRojic6NBDvYnurrvM0kEAACB9rFkjTZli13r0qHR3clQEdthB6tfPrt1/v7RyZQwODgAAEmbePGnaNLvWs2fYHydHhWHHHaVTT7Vro0dLc+dGPDYAAJAAwc1UffpIWbFrPyBHSWrVSvrf/+zaI49IS5bEbWwAACAOxo61t/v0CXuCg0j4NkcFz4a+ZIk0alRCxgX4GU3oADLHwoXSV1/Ztd69N3+bySGrY0epbl27FpfZ0B3HOxv6zz9Lr7wSh5MBAIC4ef99M3NSuZwc6cgjK92dHBWh4NnQV62SHnggRgcHAAAJEbx6TEGBtO++YX+cHBWmW26xV+XZtEm67baIxwYAAOLs77+lH3+0a336xPQU5KgyV165ecItSdKGDdLIkXEbGwAAiLHp06Xff7drMc5NwXybo/bc06z8HGjYMLNaMYC4oQkdQOaYNMmekbt+femQQyRJJSXeSSczKWRlZ0tdu9q1uDShS1KvXtL229u1IUPMUskAACA9TJxobx94oNSwYchdyVFR6NxZ6tvXro0caZrRAQBAegjOTcccYzdLV4EcVQPt20vnnGPXnnpK+vPPCA8IAADiKngW9KZNpf33j9nhyVEBtt5a6t/fro0aJa1YEYeRAQCAmAvOTVtuaZql48T3OeqWW+w3ly+XHnww3sMCfI0mdACZY/x4e/uYY6RatSSZ6zCB/elSZoUsybvkTNya0LOypDvvtGvz50t33BGnEwIAgJgqKZHeesuu9exZ6e7kqCjdcIO9vXIlF7sAAEgXa9dKH31k16rITcHIUTV0ww1Sfn7FdkmJdNNNURwQAADETXAz1fHH27N1R4kcFeSaa+zV9tas4foSAADpIlRuyopfy6bvc9Ruu0nHHWfvcM89TBAFxBFN6AAyw+rV0ocf2rVevTZ/G7zUjOSzkBVrvXpJRxxh1+69l9mpAABIB19/LS1datd69Kh0d3JUlHbaybus4ogRLP0HAEA6mDxZKiys2M7JkY48MuyPk6NqqGVL6aKL7NrLL0s//hjFQQEAQMzNni19+61dC772ESVyVJDttpP69bNr99wjLVgQ83EBAIAY+vtv6aef7FrwCroxRo6Sdzb0lSvNSsUA4oImdACZ4Z137JuCubnS0Udv3gwOWXXqSLVrJ2hsCRIcsubNkxYvjtPJHEd64AHz37lcUZG5URj8SCUAAEgtEyfa2507S+3aVbo7OSoGbrzR3l6xwiybDAAAUtukSfb2QQdJDRuG/XFyVASuvNL73/i666I4IAAAiLng2TwbNzY5KYbIUSEEZ6I1a6QhQ2I+LgAAEEPBualZM2nffeN6SnKUpC5dvM3+I0dKy5fHfWyAH9GEDiAzvPGGvX3oodYNq+CQlWlP+UnS9tub8BgorrOhd+woXXqpXXvvPW9jGwAASC3BzVQ9e1a5OzkqBnbeWerd267de6+5WQgAAFJTSYn05pt2rYrVY0IhR0WgcWPpiivs2ttvS59/HsVBAQBATAU3U/XqZU9aFAPkqBB22kk64wy79tJL0pQpMR8bAACIkbFj7e3jj5eys+N6SnJUmZtvNhNsllu92qxUDCDmaEIHkP42bZLeesuu9eplbfohZGVnS1272rW4NqFL0vXXm6WSA116qbRhQ5xPDAAAIvLvv9L06XatmmYqclSM3HCDvb18uTR6dIxPAgAAYuarr6SlS+0aTegecclRF19sZgYLdO21rL4HAEAqmD9f+vJLu9anT8xPQ46qxLBhUqNGdu2CC+zVogEAQGqYPVv67ju7FofcFIwcVaZzZ+mkk+za/fd7r/cBiBpN6ADS35Qp9iySjiMdd5y1S3DIatw4AeNKguAlZ+LehF6/vnTPPXZt5kxp+PA4nxgAAEQkeBb0pk2lPfao8iPkqBjZZRfvrPP33GNmXgAAAKnnhRfs7R13lNq1q9EhyFERqldPuu46u/bZZ2YFPgAAkFyvv25vN2xoVieOMXJUJZo1k+64w679/rt0332xHBYAAIiF4NVjmjSRDjww7qclRwW48UYpK6A9du1a+pmAOKAJHUD6e+MNe3uvvaQWLazS8uX2Lpn4pJ+UhCZ0STr5ZGn//e3a0KHSrFkJODkAAKiRiRPt7e7dq132jxwVQzfeaG8vW2Zm9QQAAKll40bplVfsWvDMSWEgR0Vh4ECpbVu7du21UmlpDA4OAAAiFtxM1bOnVKtWzE9DjqrCwIHeD95yizR3bszGBQAAYiA4Nx13nJSbG/fTkqMCbL+9dOqpdm3UKGnRoriNC/AjmtABpLfSUmnCBLvWq5dnNz8sNyN5Q9bcudKSJXE+qeOYkBb49ODGjdJll8X5xAAAoEZWrZI+/dSu9ehR7cfIUTE+SfBs6A89JH3xRYxPBAAAojJpkrRypV3r37/GhyFHRaFWLenmm+3atGnS2LFRHhgAAERs0SLvtaU+feJyKnJUFbKzzfUkx6morV8vXXppzMcHAAAiNH++9OWXdq1v34ScmhwV5MYb7Qm51q+X7rorrmMD/IYmdADp7euvpYUL7Vrv3p7d/BKytt9eqlPHriVkNvSdd5YuuMCujR8vTZ6cgJMDAICwvPuuVFxcsZ2XJx1+eLUfI0fF2MiRUn5+xbbrSuecI23aFIeTAQCAiDz7rL198MHeWbnDQI6KUv/+5uCBbrjBzrQAACBxxo831zHK1asnHXFEXE5FjqrGHnuY60mBxo2T3nsvZmMDAABRGD/e3m7YUDr00IScmhwVpEMH6Ywz7NqDD5rJDgDEBE3oANJbcHDr1EnadlvPbn4JWTk5Uteudi0hTeiSWepviy3s2kUXSYWFCRoAAACo0sSJ9vYhh5ibhdUgR8VY+/bSbbfZtd9/l+68Mw4nAwAANbZokXl4L1DwjaowkaNicODg3PTnn96HBAAAQGKMG2dvH3us/aB9DJGjwnDnnd77coMHm9WKAQBAcj3/vL3ds6eZHCoByFEhXH+9lJtbsV1SIp19tlRUFK/hAb5CEzqA9OW63ib0Xr1C7uqXkCV5l5xJWBN6QYF3yZoZM6T770/QAAAAQKWKi6V33rFrPXuG9VFyVBxcfLHUrZtdGzpU+vXXOJ0QAACE7aWXzI2ocnXqSH36RHQoclQM9OnjPfgtt9BcBQBAoi1dKk2ZYtf69o3b6chRYWjSRLr7brv299/S8OExGRcAAIjQtGnSN9/YtX79EnZ6clQI7dpJ115r1378URo2LB7DAnyHJnQA6ev3383FlEC9e4fc1c8h67vvEnjys86Sdt/drt16qzR/fgIHAQAAPL74Qlqxwq517x7WR8lRcZCTIz3xhPlarqhIGjDAbnoDAACJFzzLdp8+Ya0eEwo5KgYcR7rjDrs2d670yCMxOgEAAAjLhAn2NYv8fOnoo+N2OnJUmM48U9p7b7t2553SzJnRDgsAAETq0Uft7S23lI46KmGnJ0dV4tprpR13tGu33ipNnx7zcQF+QxM6gPQVPAt669behFHGzyFr7lxpyZIEnTwrSxo92twgLLd2rXTllQkaAAAACGnSJHu7a1epTZuwPkqOitPJunTxZqSvv5ZGjYrTCQEAQLV++sm8Ap1xRsSHI0fF6OBHHCEdeKBdu+MOaeXKGJ0AAABUa9w4e/voo6W6deN2OnJUmB/OypIeesh8Lbdxo3TRRTEbHwAAqIE1a6QXX7Rr55xjT0oUZ+SoSnbOy5OeesrOTYWF0tlnM0EUECWa0AGkrzfesLd79bKbn8usX+9doTeTQ9b225sJKALFbAnkcOy+uwlpgV56Sfr00wQOAgAAWCZOtLd79gzrY+SoOOeoG26QttvOrl17rTRrVhxPCgAAKvXcc/Z2mzbSwQdHdChyVAxzlOOYGT0DLV0qDR4coxMAAIAqrVghffCBXevbN26nI0fVMEd17erNRW++6b0eCAAA4m/MGDNRY7msLOl//0vY6clR1eSo3XeXhgyxa19/Ld1/f8zHBvgJTegA0tPcud51VHr1Crlr8FN+UmaHrJwcc70pUEKb0CVp6FCpUSO7duGFUnFxggcCAAA0Y4b01192rUePsD5KjopzjqpdW3r8cbu2fr103nmS68bxxAAAwKO42DtTVf/+9uxINUCOinGO2mcf74OUL74ovfxyDE8CAABCmjRJKiqq2K5VSzr22LidjhwVQY669VapRQu7dtFF5joTAABIDNeVHnnErnXvLrVunbAhkKPCyFE33yxtu61du+466e+/YzgywF9oQgeQniZMsLcLCqQDDgi5a3DIchypYcM4jStFBC85k/Am9KZNpdtus2s//+wN3AAAIP4mTbK3W7aUdt01rI+SoxKQow44QBo40K699570wgtxPjEAALC89560aJFdO/30iA9HjopDjho1yjvpwfnnm8kqAABA/IwbZ28fcYTUoEHcTkeOiiBHNWwo3XOPXZs927uaDAAAiJ/vvpOmTbNr552X0CGQo8LIUfn50lNPmf845TZulAYMkEpLYz4+wA9oQgeQnt54w97u3l3KzQ256/Ll9nZBgZSdHZ9hpYqkN6FLJkzvvLNdu+EGafHiJAwGAAAfC156t3v3sGf0JEclKEfdfbfUqpVdu+QSchMAAIn03HP29p57Sh07Rnw4clQcclSbNtLDD9u1lSulM87gJiEAAPGyerV5WC9Q375xPSU5KsIcdcop0kEH2bXhw6U//4x0WAAAoCYefdTe3mor8/BeApGjwsxR++0nDR5s1z75xPszBBAWmtABpJ/ly6WPP7ZrvXpVunvwk36ZvNRMueCQNWeOtHRpggeRk2NmqAq0cqV07bUJHggAAD62bJn0xRd2rWfPGn08EDkqTho29DZULV9uGtEBAED8rVjhXXXvjDOiOiQ5Kk456qSTTINVoClTpPvui/GJAACAJOmtt6RNmyq2c3OlHj3iekpyVIQ5ynGk0aPN/blyhYWmwcp1ox4jAACowqpV0pgxdu2ccxLeAU6OqkGOuvNOaeut7dqVV5rVZADUCE3oANLPW29JJSUV27VrS0ceWenufgxZO+xgVpAJlJTZ0Pff33tj8MknpW++ScJgAADwoddes2eFzM+XDj007I+To4yE5KiePaUTTrBrY8aY7AsAAOLr1Vft5qq8POnEE6M6JDnKiEuOGj3azIoe6JprpF9+icPJAADwuXHj7O1DDzVTasYROcqIKEd16iRdeqlde/99aezYiMcGAADC8MIL0vr1Fds5OdLZZyd8GOQoI6wcVa+e9Pjjdm3tWmngQB7gA2qIJnQA6Wf8eHv7iCOkunUr3d2PISsnR+rSxa4lpQldMkv91atn1wYPZplkAADizXVNg06gI4/0XompAjnKSFiOevBB743c88+X1qxJ0AAAAPCpZ5+1t3v0kBo3juqQ5CgjLjmqUSPzM3OcilphoXTqqdLGjXE4IQAAPrVunfT223atb9+4n5YcZUSco268UWrd2q5deinXlwAAiBfXlR591K4dd5zUsmXCh0KOMsLOUYcdJg0YYNfee897rRBAlWhCB5Be1q+X3n3XrvXqVeVH/BiyJKlbN3s7aU3orVqZC16Bvv1Wevrp5IwHAAC/+PRT6ddf7dp559XoEOQoI2E5qnlz6d577drcudK11yZoAAAA+NBff0lffWXXzjgj6sOSo4y45aiDD5Yuv9yu/fKLdP31cTohAAA+9M470oYNFdvZ2aahKs7IUUbEOapePWnkSLs2f750yy0RHhAAAFTpq6+8q7MNHJiUoZCjjBrlqHvukbbc0q5deqm0YEHU4wL8giZ0AOnlgw/sC15ZWWZ2qir4NWTttpu9nbQmdEm6+GKpY0e7dvXV0ooVyRkPAAB+EDwL+rbbSocfXqNDkKOMhOaoM880S1sHGj1a+vLLBA4CAAAfee45e7tpU+moo6I+LDnKiGuOuv12aeed7dqIEdJHH8XxpAAA+MiLL9rbBx0kbbFF3E9LjjKiylF9+piVpAPdd593wgoAABC94FnQt9nGe58nQchRRo1yVMOG0iOP2LWVK81Kxa4b7dAAX6AJHUB6GT/e3t5//2oveBGyjNmzpaVLkzMW5eVJDzxg15Yulc45RyotTc6YAADIZPPnS6+/btcGDTIP8NUAOcpIaI5yHOmxx6T8/Iqa65rlADdtStAgAADwidJSbxP6KadIublRH5ocZcQ1R9WqJb3wgvlaznXNTPZMfAAAQHT+/FOaMMGu9e2bkFOTo4yocpTjSKNGmftz5UpKpAsuoJkKAIBYWr5cevVVu3buuTW+Hxcr5Cijxjmqe3fp1FPt2oQJ3p8tgJBoQgeQPoqLpUmT7Frv3tV+zK8ha4cd7N4lKcmzoR9xhPfnNW6cdNttyRkPAACZ7NFHzY2lcnXqmBm2a4gcVSGhOap9e29G+v136c47EzgIAAB84NNPpTlz7NoZZ8Tk0OSoCnHNUTvtJA0datfmzTMNVgAAIHIjRtjNyg0amIf1EoAcVSGqHLXtttKVV9q1Tz81D/EBAIDYeO45aePGiu3c3Ijux8UKOapCjXPU/fdLzZrZtcGDpSVLohob4Ac0oQNIH59/7k1Mxx1X7cf8GrJycqQuXexaUpvQJWnkSKlRI7t2882mGR0AAMRGYaGZSTtQ//7ev4PDQI6qkPAcdfHF3qkbhg5l2WQAAGLp2Wft7Z12krp2jcmhyVEV4p6jLr7Yu8z1mDHSSy/F+cQAAGSoxYulZ56xa+edZxrRE4AcVSHqHHXNNdLWW9u1IUOkBQuiPDAAAJDrmkmhAvXp421kTiByVIUa56gmTcxKMoGWLjXXnQBUiSZ0AOnjjTfs7a5dvRdOQggOWY0bx2pAqS+4bynpTehbbWWWqwleeuj006WffkrOmAAAyDTjxkmLFtm1CGeCJEdVSHiOysmRnnxSys6uqBUVSQMG2LPcAwCAyKxbJ40da9dOP11ynJgcnhxVIe45KivLNMoFP3Q5aJB3pnsAAFC9UaOkTZsqtnNzE9p8Q46qEHWOqlNHeuABu7Z4sdSrl7RhQ5QHBwDA5z77TPrjD7s2cGByxlKGHFUhohzVt690/PF2bcwYacKEiMcF+AFN6ADSg+t6m9B79672Y6Wl0ooVds0vT/pJKdA8Fcrhh5tlHAOtXy/17GkufAEAgOiMHm1vH3CAmdWzhshR9nZSclSXLt5lk7/+2jsTAwAAqLnx46W1ayu2s7KkU0+NyaHJUfZ2QnJU69bSI4/YtVWrzBLYpaUJGAAAABli3TrvtaVTT5VatUrI6clR9nZMclSPHuYeXKBvvpHOOsvcfwUAAJEJvg7RsaN04IHJGYvIUTHJUY5jsnBBgV0//3xp5cpIhwZkPJrQAaSHH3+UZs+2a716VfuxlSu910/8HLJmz/Y++ZgUF10knX22XZszxzxVWFiYnDEBAJAJpk2TvvjCrg0eHNGhyFH2dtJy1I03StttZ9euvVb6998kDAYAgAzy7LP29pFHSi1bxuTQ5Ch7O2E56sQTpdNOs2tTpkgjRybg5AAAZIhnnpGWL7drQ4Yk7PTkKHs7ZjnqiSekdu3s2iuvSLfcEoODAwDgQ0uXmpWJAw0cGLMV9iJBjrK3I85RLVpI991n1xYskC6/PNKhARmPJnQA6SF4FvT27cOa0TNUoPBTyOrUSapd266lxGzojiM99JC0zz52/bPPTKMcMy8AABCZ4JmqWrUK68G9UMhRKZKjateWHn/crq1fL3XvniJPFwIAkIbmzpU+/NCunX56zA5Pjkpijho1Smrb1q5de630888JGgAAAGmspMS7ku0xx0idOydsCOSoOOWopk2lN9+UGjSw67fcIo0ZE4MTAADgM888Y0+wWKuWdMYZSRuORI6KaY7q3186+mi79tRT0uTJER4QyGw0oQNID+PH29u9eoX1BGFwyKpdW6pTJ3bDSnU5OVKXLnYtJZrQJRPCX3/dLJcc6PHHTYM6AAComeXLpZdesmsDB0q5uREdjhyVQjnqgAPMzzLQ77+bRvR165IzJgAA0tkLL9gPwDdsKB13XMwOT45KYo5q2FB67jn7umFhoXTqqdLGjQkaBAAAaer1170rryVwFnSJHBXXHNWpk5n9PCuoReSss6SpU2N0EgAAfKC0VHr0Ubt2wglS48bJGU8ZclQMc5TjmJ9x/fp2fcAAadGiCA8KZC6a0AGkvr//ln75xa6FOaNncMjy01N+5YKXnEmZJnRJat5cmjBBys+36xdfLH30UXLGBABAunr6aWnDhortnBzpnHMiPhw5KsVy1LBh0g472LWpU6V+/aSiouSMCQCAdOS6pkk5UL9+3msTUSBHJTlHHXigdMUVdu3XX6XrrkvgIAAASDOuKw0fbtd220066KCEDoMcFeccddRR0v3327VNm8wDmbNnx/BEAABksClTTB9ToPPOS85YApCjYpyj2rTx5uO5c02eWrUqigMDmYcmdACpb+RIe7tpU2mffcL6KCErxZqnQtl1V7NUUaCSEvOk6D//JGVIAACkndJS70oifftKLVtGfEhyVIrlqAYNpPfe864i8/bbZuaF0tLkjAsAgHTz7bfSH3/YtRgvl0yOSoEcdeut3umvRoyQPvwwwQMBACBNfPqpyUmBrrgirFWJY4kclYAcNXiwNGiQXVu8WOrRQ1qzJsYnAwAgAwXPgt65c9g9TPFEjopDjjrnHOngg+3ajz+a3LR+fZQHBzIHTegAUtvcudITT9i1k06SsrPD+jghS+rWzd6eNcv73yXp+vWTbrjBri1fLvXsKa1enZwxAQCQTt5917tc8uDBUR2SHJWCOapNG9OIHryk43PPSVdfnZwxAQCQbp591t7eZpuY3ygkR6VAjqpVS3rxRfM10BlnmGtOAADAFjzL49ZbS336JHwY5KgE5aj775eOOMKu/fKLdPLJZqIoAAAQ2sKF0vjxdm3gwIQ/uBcKOSoOOSorS3rpJal9e7v+2WfSiSeyUjFQhiZ0AKlt6FCpsLBiOy9PuvLKsD9OyJI6dZJq17ZrP/yQnLFU6eabpV697Nr06dKpp3LBCwCA6owaZW936RJ1MxU5KkVzVKdO0ptvSvn5dn34cOnee5MzJgAA0sWmTdLLL9u100+P+Y1CclSK5KjOnaW777Zr8+dLRx4prViR4MEAAJDCpk+X3nrLrl12mZSTk/ChkKMSlKNycqRXXpG2396uv/VWje7DAgDgO08/LRUXV2zn50v9+ydvPAHIUXHKUS1aSO+/b74GevNN6eyzWakYEE3oAFJZqFnQzzlHat067EMQssx1pODVhxO+BHI4srKk55+XdtrJrr/5pneWdAAAUOHvv6V33rFrgwdH3UxFjkrhHLX33tJrr3lXBxoyxMyKDgAAQnvrLe8s2HG4UUiOSqEcdeGF0mGH2bXvvpMOP5xGdAAAyt1zj73duLFpqEkCclQCc1SjRuYeXPB/5BEjpMcfj8MJAQBIc6Wl0mOP2bWTTjJ/p6YAclQcc1T79tLkyd6f9QsvSJdeKrluDE4CpC+a0AGkrjvvtJcuycuTrr66RocIDlmNG8dgXGlot93s7ZRongqlXj1pwgRvGh46VBozJjljAgAg1T38sL3dqJF0yilRH5YcZaRsjjr2WOmpp7z1s8+W3n478eMBACAdPPusvX3AAVK7djE/DTnKSIkclZUlPfOM1KqVdzCHHeZ9KAEAAL9ZsMA0zwQaNEiqWzcpwyFHGQnLUdtsI40fL+Xm2vVBg6QpU+J0UgAA0tTkydKsWXZt4MCkDCUUcpQRtxy1005mgovglYofeEC67bYYnQRITzShA0hNc+ZITz5p1849t0azoEs86VcuOGR9911yxhGWdu2ksWO9yzyefXaKDxwAgCRYv97biHz22VKdOlEfmhxlpHSOOv10adgwu1ZSIp1wgjR1anLGBABAqlqyxPug1hlnxOVU5CgjZXLUlluaJqrgRvQffqARHQCABx6wJ4SqVcussJck5CgjoTlq//29s7oWF0t9+kh//hnHEwMAkGYefdTe7tpV2mOPpAwlFHKUEdcctc8+0uuve/uZbrpJGjUqhicC0gtN6ABS09Ch3oteNZwFXfLeQyJkGbNmeQNoSjnoIOnBB+3axo1Sr15mVg4AAGC89JK0cmXFtuNI558fk0OTo4yUz1FXXCFdfrldW7/ezJT+++/JGRMAAKlozBjTTFMuP1/q2zcupyJHGSmVo7bbTvr4Y9OQHmjaNOnQQ1Ms4AEAkCBr1nhX2Dv9dKl58+SMR+SocgnPUWeeKV11lV1bsULq3p0H9gAAkKT586VJk+zawIHmvlyKIEcZcc9RRx0lPf+892d/4YXmvi3gQzShA0g9s2eHngU9+CZRGHjSz+jUyfTxB/rhh+SMJWznnedtops/X+rd2zSkAwDgd67rfar+qKOkDh1icnhylJEWOWrYMOm00+za8uXSkUdKc+cmZ0wAAKSaZ5+1t3v3lho0iMupyFFGyuWobbc1jejBKy3++KNpRF+6NBmjAgAgeZ54Qlq1qmLbcbwPuicYOcpISo66804zGVSgv/4yD24GThwGAIAfPfmkWYm2XN260imnJG88IZCjjITkqJNOkkaP9tbPOMO7EiPgAzShA0g9oWZBD376Pgylpd57R34NWbm5Upcudu3775Mzlhq5/34zK3qgr782gW7duqQMCQCAlPHFF9JPP9m1GC2XTI6qkBY5KitLeuop6eij7frcuebBBGasAgD43a+/eu82nXFGXE5FjqqQkjmqQwfTiN6mjV3/6SfpkEOkJUuSMiwAABKuqEgaOdKu9ewpdeyYnPGIHBUoKTkqK0t64QVpl13s+pQp0gUXmAkxAADwo5IS8/BeoFNPjdvkBpEgR1VIWI46/3zp1lvtWnGx1KeP9PnncTghkLpoQgeQWmbPNg00gSKcBf3dd6UNG+xaBIfJGMFLziT9pl84cnOl116T2rWz6xMmSPvvz8yeAAB/C37Cvn1703AcA+QoW1rkqPLctOeedn36dKlHD2n9+uSMCwCAVPDcc/b2lluama/jgBxlS8kctc02phG9bVu7/ssvphF98eKkDAsAgIR69VXvPZYrrkjOWMqQo2xJyVF160oTJ0otW9r1xx+X7rsvAQMAACAFvfOONzcNHJicsVSCHGVLWI66/nrp4ovt2saNUvfu3onEgAxGEzqA1HLnnd5Z0K++OqJDjRplb3fp4r235CcpedMvHFtsYS541atn16dNk3bf3cyMDgCA3yxYII0da9cGDTIzFsUAOcqWNjmqbl3prbek7be3619+KfXrx9LJAAB/Ki42MzoGOu00KTs7LqcjR9lSNke1b28a0bfayq7/+iuN6ACAzOe60vDhdm3vvaV9903OeMqQo2xJy1GtW5v7cvn5dv3yy6WHH2ZGdACA/zzyiL3drZu0667JGUslyFG2hOUox5FGjJD697frq1ZJRx4p/f13nE4MpBaa0AGkjlCzoA8cKLVqVeND/f23eRgx0ODB5u9/vwoOWTNnSsuXJ2csNbbjjuaCV6NGdn3RIunAA6WXXkrKsAAASJrHHjMNVeXy86WzzorJoclRXmmVo5o0kd57zzvFxVtvmRWGuFEIAPCbDz4wD/AFOv30uJyKHOWV0jmqXbvQjei//SYdfLC57gQAQCb68EPvzIxJngWdHOWV1BzVrZt3NSHXNZNgDBwobdqUoIEAAJBkc+Z4Q8p55yVnLJUgR3klNEdlZUlPPmlWJQ60aJF0+OHSf//F6cRA6qAJHUDquOMOu5mqdu2IZ0F/6CF7u1Ej6ZRTIh9aJujc2UwsH+iHH5IzlogcfLCZ9Xy77ez6pk3SqadK110nlZYmZ2wAACRSUZH06KN27dRTpcaNY3J4cpRX2uWotm1NI3rwA3zPPGMa0TduTMaoAABIvNJSs+peoG7dpE6d4nI6cpRXyueorbeWPvnEfA00fbq5FrVwYTJGBQBAfAXPgr7ttlLPnskZSxlylFfSc1TfvtLtt3vrjz9uclLwg54AAGSiJ56w+1AaNJBOOil54wmBHOWV8ByVmyu98op0wAF2fdYs6YgjUmhGBiA+aEIHkBpmzZKeftquDRwotWxZ40OtW+edUP1//5Pq1Il8eJkgN9csuRMoZZZADtd220lTp0qHHeZ97847zQWxtWsTPy4AABJp/HjvTZ4LLojJoclRoaVljurcWXrzTfNgZ6AnnpD22kuaMSM54wIAIJEefFD67DO7dsYZcTkVOSq0tMhRW21lGtHbtbPrv/9OgxUAIPP89JM0ebJdu+wyKTs7OeMROaoyKZGjrr3WvIJ99ZWZYnTq1AQPCACABFq61Dsp1GmnSXXrJmc8IZCjQktKjsrPlyZOlHbZxa7/9pt07LHSihVxHgCQPDShA0gNd97pnQX9qqsiOtSLL0qrVlVsO450/vlRji9DBC85k3I3/cJRUCC9/XboZrvx46X99jNLIgEAkKlGj7a3991X6to1JocmR1UuLXPUvvtKr77qvZH800/mF/T888kZFwAAifDXX9I119i1rbeWzjorLqcjR1UuLXJU27bSxx9L7dvb9T/+oBEdAJBZ7rnH3m7aNG4P6YWLHFW5pOcoxzErWb/yirebbcEC6cADvZ1vAABkAteVzj5bWrzYrg8cmJzxVIIcVbmk5KiGDaV33zUrDQWaOtXcy/3iiwQMAkg8mtABJN/Mmd5Z0M87L6JZ0F1XGjXKrh1zjLTNNlGML4Mk/WJVrOTmmh/0Qw+FbqrafXczCwMAAJnm55+lTz+1azGaBZ0cVbW0zVE9epirkPn5dn3dOun0000j3rp1yRkbAADxUlJi/o7bsMGuP/10XGarIkdVLW1yVHkjevAPbsYM6aCDpHnzkjEqAABiZ+5c6eWX7drgwd5rBglEjqpayuSofv2kL780D3UGKiw0060OHiwVFSVlaAAAxMXo0dKkSXate3dp552TM54QyFFVS1qOatZMev99acst7fqcOdIBB0i33mquXQIZhCZ0AMkXahb0K6+M6FCffSb98otdGzw4irFlmOCQ9e+/ab7iy/nnm2UjCwrs+uLF5uYgs3sCADJN8CzozZtLffrE5NDkqKqldY468UTp22+lzp297z3zjHmAL/iHDwBAOrv/fu/MQoMHm2sFcUCOqlpa5ag2bUwjeocOdv3PP82N5uefN3d5AQBIR/fdZ9+Py8+XBg1K2nAkclR1UipHdekiffeddOih3vdGj5YOO8w7WywAAOno55+lIUPsWrNm0uOPJ2c8lSBHVS2pOWqrrUwvU9Omdr20VLrpJumQQ8wDokCGoAkdQHLNnGkaXwJFOAu65H3Kr0MH6YgjIhtaJurcWapVy6798ENyxhIzhxwiff211LGjXS8sNLN7Xn21CXIAAKS7lSulF16wawMHSnl5MTk8OapqaZ+jOneWvvlGGjDA+97vv0t77GEuoNJUBQBId3/8IV13nV1r31666664nZIcVbW0y1GtW5tG9OClk1esMNeaevaU5s9PytAAAIjYypXSY4/ZtbPPlrbYIinDKUeOqlrK5agmTaR335Uuu8z73qefSt26pfCyNwAAhGH9eunkk6VNm+z6M89ILVokZUiVIUdVLek5qlMnc8JQk2J8+ql5wO/11xM4ICB+aEIHkFx33OGdBf2qqyI61Pz53r+fL7hAyuJPus1yc72rA333XXLGElPbbitNnRo6Ud99t9S7t7RmTeLHBQBALD3zjLn4VS47Wzr33JgcmhxVvYzIUXXqmEbzF1+U6tWz39u40fx+OuUUafXq5IwPAIBolZRIZ51l/l4r5zgmR9WtG5dTkqOql5Y5asstTSP6dtt533vzTXMn8+mneYAPAJA+Hn1UWru2YjsrK3QjcQKRo6qXkjkqJ0e6916zQkzt2vZ7c+dK++1nrj0BAJCOLrtMmj7drl16qXT00ckZTyXIUdVLiRzVurX0wQfS7beb+7qBVqwwq12fd559/xdIQ/zRAyB5/v1XevZZu3b++RE/Pfjoo+ZeY7k6daQzz4x8eJkqeMmZjJmQoFEj6a23pIsu8r43caK0777SrFmJHhUAALFRWmqWtQ10/PGmOSYGyFHhyZgcdcopZvaFXXbxvvfyy9Kuu6bxLw4A4GsjRpiH1ANddJG0//5xOyU5KjxpmaNatZK+/FI66STve6tWmdljjzmG5ZMBAKmvsFC6/3671qePWS0michR4UnZHHXaadLnn0tt2tj1jRvNe5dfbk9EBgBAqnv9dRNQAu2yizR0aHLGUwVyVHhSIkdlZ5tVGz/7TNpqK+/7jz4q7b679MsviR8bECM0oQNInuBZ0PPzpSuvjOhQmzZ5s2D//qYvGbaUCFnxkpNjLqQ++qj5PtAvv0g77WSeMFy3LjnjAwAgUpMnS3//bdcGD47JoclR4cuoHLXtttJXX0kXXuh9759/pL33lh54gNk9AQDpY/p06YYb7FqHDtKdd8btlOSo8KVtjmrSRBozxtyIbt7c+/6775pZ0R9/nNwEAEhdL70kLVhg1664IjljKUOOCl9K56jddjNTih5wgPe9ESPMrLHLliV+XAAA1NTcudKAAXatTh1zTaBWreSMqRLkqPClVI7ae2/pxx+lE0/0vjd9umlEHz2a60tISzShA0iOGM+CPm6ctHixXbvgggjHluGCQ9a//5pVXjLKueeaZr3Gje362rXmhvR220lPPmk/GgoAQKqaP1865xy7tuOOMZvRkxwVvozLUbVqmUbz11/3Xp0sKpIuvtjMuL98eVKGBwBA2IqLzXRPmzZV1BxHeuYZc8MwTshR4Uv7HNW7t7kheNpp3vfWrDHXoo44glX4AACpZ9kyMzlPoAMOME0uSUSOCl/K56hmzaQPPgj9A/zgA/N77ZNPEj8uAADCVVJi/r0f/BfsqFFSx47JGVMVyFHhS7kc1aiRebDhySe91yw3bTITkPXqxUN8SDs0oQNIjttvtxuAo5gFXTLZL9CBB5pJr+HVubOUl2fXfvghOWOJq4MPlr7+WtphB+97//1nnmLt0kV6+22eJAQApK7Vq6VjjpHmzbPrgwebxqoYIEeFL2NzVO/e0rRp0p57et974w2z3OTHHyd6VAAAhG/4cOnbb+3aZZdJ++4b19OSo8KXETmqcWPp+eelSZOkVq2873/wgfkN8PDDUmlp4scHAECwNWvMTNT//GPXkzwLukSOqom0yFG5ueaH+sQT3sHOnCkddJB01FEpOHAAAGRW0fv0U7t24olmwoMURI4KX0rmKMeRzj7bDKRrV+/7EyeaXibuyyGN0IQOIPH++Ud67jm7NmhQ6CVtw/D999JXX9m1wYMjHJsP5OVJO+9s11Jq6b5Y6tDB/OY466zQjXq//SYde6x02GEpkDQBAAhSWCj16SP9/LNd32cfc3EiBshRNZPROWrrraXPPgt9I3rOHPOA3yGHSB99xAN8AIDU8uuv0k032bWOHaXbbovraclRNZNROap7d/P7LtTN6LVrzXXOQw8102sBAJAsGzdKxx3nfVCvWzcz4UESkaNqJq1y1P/+Z2Y9b9nS+95775npSPv1k2bMSPzYAAAI5YsvpJtvtmtbbSU98kjMJoOKJXJUzaR0jurYUZo6Vbr0Uu978+ebe3LXX2/uFwMpjiZ0AIl3xx3eWdCjmHVh9Gh7e8stzXU1VC54yZmUCVnx0LCh9NRT0o8/SkceGXqfjz4y/1H695dmz07o8AAACMl1pXPOMbMpBtp2W2nCBDO7UAyQo2ouo3NUbq40bJhZKWaLLbzvT5liGqr220965x2a0QEAyVdUZBqBi4oqallZ0jPPmOtNcUSOqrmMylEFBdLTT5vc1Lq19/2PPzbTkD34ILOiAwASr7hYOukk8+/4QG3aSOPGmbyUROSomkurHLXXXmaAe+8d+v3XXjPTkg4YIM2dm9ixAQAQaOVK6ZRT7H+3Z2dLL70kNWqUrFFViRxVcymdo2rVkkaMMNeXmja133Nd01/Xvr00cqSZ+ABIUTShA0isv//2zoJ+wQURz4K+bJnJf4HOOy9mfVkZq1s3ezulQla87Lyz9O670uTJZumaUF54wTxteOWV0ooViR0fAACBbrrJm5maNjWNv6GagyNAjoqML3LU0UebB/gOOCD0+19+aWZN23136Y03aKwCACTP3Xd7/zIeMsQ0vsQROSoyGZmjjj7azIo+YID3vfXrpYsuknbd1TSsb9yY+PEBAPyntNTMRj1hgl1v2lR6/32pbdvkjKsMOSoyaZejWrY0M6KPHi21aOF9v6REevJJs6LxpZdKS5YkfowAAH9zXencc81KsIFuvtmsSJyCyFGRSYscdfTRZmXsww/3vjd/vnTZZWaG/ltuMb8RgBRDEzqAxIrxLOhPPilt2lSxnZtrJg1F1YKf9PvnH/OQpy8cfrhJlc8+G3qmqk2bpOHDzYWvkSPt32AAACTCE09It91m1/LzpTfflLbZJmanIUdFxjc5asstzWoxDzwgtWoVep/vv5d695a6dpVeecXO+QAAxNtPP0m33mrXdtjB3IyJM3JUZDI2RzVsKD3+uJn4IFRj308/SWefbWafvf56c/MQAIB4cF3T0Bs8sUGDBtJ775lJeJKMHBWZtMxRubnSoEFmgrK77go9o2xhoXTffWaGz5tuklavTvQoAQB+9dRTZnWOQAceKF1zTXLGEwZyVGTSJke1aGEm1hw+XMrJ8b6/fLl5SGKrrUxT+rx5CR8iUBma0AEkzl9/Sc8/b9cuuEBq1iyiw5WUSA89ZNdOOCHiSdV9pXNnKS/Prv3wQ3LGkhTZ2dLpp0t//ikNHWouwAZbvtwEtx12ML9vN2xI/DgBAP7zzjtm2oJAWVmmwXePPWJ2GnJU5HyVo7KzpQsvNFfkHn7YXNgK5ZdfzDLfnTubG91FRYkdJwDAf4qKpDPPtP/OycqSnnlGql07rqcmR0Uu43PU4YebWdGD83y5pUvNBB1bbWWy05dfmmZBAABi5dZbzcPkgWrXNhMb7LJLcsYUgBwVubTOUXXrSlddJc2cKV17rVSnjneftWvN79/27aV77+WeHAAgvv74w6xcFqhxY+mFF8x9kRREjopcWuWorCyzyuPUqZWvVrxunZlQs317szLfjBmJHSMQAk3oABLj+++lQw6xZ0esUyeqWdDffluaPduuDR4c8eF8JS9P2nlnu5aSS87EW36+dPXVprHqootCP004c6ZpWG/eXDrrLOnDD5nlEwAQHz/8YK4YBf89M2qU1KNHTE9FjoqcL3NU7dqmmeqvv8zsIB06hN5vxgzpjDPMzGqPPcaKMgCA+LnzTunHH+3aVVfF9KG9ypCjIueLHFW/vnl478MPpe22C71PSYl5yHTffaXddzcP8ZGbAADReuABMzNioJwcaexYaf/9kzKkYOSoyGVEjmrUyDyQ988/5gefm+vdZ9ky03i17bbm2hITHQAAYm3TJunkk6X16+36k09KrVsnZ0xhIEdFLi1z1G67SZ98In3xhdS9e+h9iorM79sddpD69k2DXxQyGU3oAOLv5Zel/fbzLgUSxSzokunHCrTrrtJee0V8ON8JXnLG13lkiy2k+++Xfv/dNP+FsmaNmVHtsMPM0spDhkjTpjFjFQAgNmbNko491jy9Huiqq6Tzz4/56chR0fFtjsrNNQ/l/f679OKLUqdOofebOVMaONA0q99zj/n9DQBArEybJt1+u13r3Fm66aaEnJ4cFR3f5KhDDpGmT5cmTjTXkirz/ffmIb62baUbb5T++y9xYwQAZI7nn5cuvtiuOY550OnYY5MzphDIUdHJmBzVooX04INmMoPTTze/V4PNn2+uLbVsab5+9BETRAEAYuPqq70TG5x/vtSrVzJGEzZyVHTSNkfts480aZL088/SqaeGnqnfdaVx46Ru3aQjjpCmTKGPCQlHEzqA+Cktla67zjxFuHGj/V6HDtI110R86BkzpMmT7drgwaGvUyC04JA1aZJZkdHXOnSQXn1V+uorMxtVZf77zywHuOuu0o47mhnYaK4CAERqxQrpmGOkhQvt+sknm79jYowcFT3f56icHOmUU6RffjEXtipb0nvePLPyUbt2Jjfdequ5UMbFLwBApAoLTcNucXFFLTtbevZZqVatuJ+eHBU9X+Wo7GyzotH770u//WZWlqlTJ/S+ixdLt90mbbWVyVlTp5KZAADhmTDBPDAebPRoc20pRZCjopdxOapdO5Pjf/ml8sa/ZcvMjOiHHiq1amUmOPv0U3MPGgCAmnr7bem+++xa586m9yOFkaOil/Y5aqedpBdeMCsWDxpU+XXQ9983EyPstZeZJT343jMQJzShA4iP1avNBYNQjVMHHCB9+aVUUBDx4R96yN5u3Fg66aSID+dLwStUr18v9exp8rXv73HttZf02WfSG2+YC1tVpffp083DFu3amSUtH31UWr48YUMFAKS5TZtMZvr9d7t+0EHS009LWbH/Jxs5KnrkqDJZWdLxx5spI958U9pzz8r3nTbNzFDbpYt58O/yy6XPP2cWKwBAzdx2m2lSCXTttd47SXFCjoqeb3NUp07Sww+bh/TuuUfaeuvQ+xUXS2PGSHvvbXLTVVeZmT83bUrocAEAaWLKFOnEE73/tr7jjrisrBcNclT0MjZHde4sjR9vHsI75JDK91u82PxGOvBAqU0b6ZJLzP1mGtIBAOFYsEA680y7Vru29PLLUn5+UoYULnJU9DImR7VrZx42nT3bzOrfoEHo/b75RhowwKwqs+ee5t8HTBKFOKIJHUDs/fOPuVEyaZL3vYEDzZNXTZtGfPg1a6RnnrFrAwakfC5MOTvvbFZiCeS60pAh5r9nYWFyxpUyHEc67jjpgw+kuXPNDcKuXav+zOefm1mtWrQwDYWvvGIuigEAEEppqZnJ89NP7Xr5jZc4zOZJjooNclQQxzHLe3/1lcn6Bx5Y9f7//iuNGGEe4GvZ0vxHe+st7+pJAAAE+vZbaehQu7bzztL11yfk9OSo2PB9jiooMA/j/f23mfygqkarX36Rhg0zEyQ0aSJ17y49+KD055/cNAQAmGzUs6f3QaUhQ6JaiTgeyFGxkfE5as89pQ8/NNeWqlqtWDIrFt9/v9lv663Nf4RvvyUjAQBCW7DArDq2ZIldv/des/J9CiNHxUbG5ajmzc110jlzzNdmzSrf95tvzPXTLl1Mbho8WHrvPSY8QEzRhA4gtj78UNp9dzM7dKCcHPN43iOPSHl5UZ3ihRfMROvlHCflJnRIC45jVmk85RTve089JR1+uLR0aeLHlZK23NLcIJw2zSyffO21ZnnkyhQVmf+4J51kwt9225nlMJ94wsx0y0UwAIBknlB/5RW71qqVWQ6wUaO4nJIcFRvkqEo4jnTYYdLHH5tVZc480zRMVWXJErMkYPfu0hZbSCecIL30krRyZQIGDABIC6tWmX+HH3CAPctnTo65CxfldaZwkaNigxxVJjvbTH7w4YdmJqpzzqn6DvK6deahvYsukjp2NDNfDRwovf66+X8EAOAv06dLRx8trV1r1//3P/MAU1WruyYBOSo2fJOjDjvMTPr0zz+mqWqXXaref+5c00S4xx7SNtuYa65ffMFkBwAAE0BuuMGszvrxx/Z7xx2XFoGEHBUbGZujGjY02WfWLNOTV9nKe+XmzDEzqR91lLkn17ev9Oyz3gc0gBryfRO64zhNHce53XGcXx3HWes4zjLHcb50HGeQ4zi5MTzPvo7jvOw4zhzHcTaWfX3ZcZz9YnUOIKlc18zEc+SR0ooV9ntNmkiTJ8ckCbmuNGqUXevRo/q/RxFa7domtN5xh/e9Tz8112t++y3x40ppnTqZ/2D//mv+Iw0caGaxqspff5kb4+ecYz6/xRbmN+5dd5kGLS6EIU2Ro4AojB4tDR9u1+rVM40lbdvG5ZTkqNgiR1Vjv/2kp5+WFi40y4NffHH1v7fXrZPGjpVOPdX8G2LHHc1qAQ8+aGZZX78+MWMHEoAcBYShsFB64AHTSDJ0qPffztdfX31DSoyQo2KLHBVkp52kxx4zDVR33x3evwdmzzaf6dPH5KZ995VuvVX6+mv7YQ0gA5Gj4HuzZplpHJcts+t9+0qPPppyDejkqNjyVY5q3940Vf3wgzRjhnTbbSY3VWXmTJOn9ttPatDATJw2eLD0/POsJgOIHAUfCbymdPvt3nsLW25pJsdJsdwUjBwVWxmdo/LzTU/eX3+ZCdD69pXq16/6M2vXSuPGmQmlmjc315buukv67jt6mFBjjuvjoO04zp6SxktqKek9SZMk1ZF0lqQdJH0jqbvrulE97uE4zs2SbpS0XtITkqZL6iRpQNn5bnNd96ZozlHFuVtLmitJc+fOVevWreNxGvhdYaF0wQVmludgO+4oTZxoZueJgSlTvKvUTp5snkpDdF5/Xerf35u/69c3GeXoo5MzrrSwaZP07rvSiy+a3+81XbYmN1fabTcT6spfVS2Xg5iZN2+e2rRpU77ZxnXdeckcTzohRwFRmDBBOv54qbS0opaTYxrQg9eCiyFyVPyQo8LkutKPP0pvvCGNHy/98kvNPp+dbR7o69bNZKdu3czygbVrx2O0qAY5KnLkKKAarmseSrrmGjP7YSh77mke6s6N2T3yKpGj4occFUJxsfTll2Zp5MmTpe+/r1nDVIMGZp3pnXYyr513NtdoGzaM35hRI+SoyJGj4HsLF0r77y/9/bddP/xwadIkqVat5IyrCuSo+PFtjpo+XXr1VfOL/OOPmn22oMB0mO25p7TXXub76lbxQ0ohR0WOHAVfKC01fz9cd515MCmUBg2kd96R9tknsWOLADkqfnyRowoLpU8+Mf9OmDTJPMwarpwcaYcdzAQg5a+uXbm2lObimaN824TuOM5Wkr6V1FTSCNd1Lw94L1/S+5L2lfSFpINd1y2K8DyDJI2WtFHSAa7rfhvw3h6SPpFUW9Ig13UfjvCXU9X5CVmIr0WLzIw7X3zhfa9XL+m556p/uqoG+vQxYaBcx47mWkOW79d1iI0ffpB69pTmz7frWVnSPfdIl1yS8g+DJt+qVeY36SuvmP8vgpfDDFezZtJ225lXx44V32+zTUpeSE5XXKyKDDkKiNDataahatAgacMG+72nnzZPmscROSq+yFER+Ocf81DG+PEmN0VyfSInR+rc2TSklzenb7cdF8ISgBwVGXIUUI3PPpOuuMLM5hxKdrZ07rlmVp4GDRI2LHJUfJGjqrFkifTBB+ZO83vvSQsWRHactm0rmtLLG9Q7dkzYwxyoQI6KDDkKvvbnn2Ym56ef9v6Fudde0vvvmxX2UhA5Kr58naNcV/r1V3M/7pVXvA9nhKtDB9OUvuee5tpShw5mReOM/Q+X3shRkSFHwRc++EC66irzl2MoWVnSWWdJN98spcnvDXJUfPkqR5XnpvKG9K+/juyeXPv2dmP6LrtILVvGfryIC5rQ48BxnFclnSBpjqTtXNfdFPR+J0m/SnIUYQByHKeZpH8k1ZN0t+u6V4fY5y5JV0laK6l9tE8Vhjg+IQvxM22adNxxZqnYYDfeKN10U0zTz5w5ZkL1wElDH3hAuvDCmJ0CMvexevWSvvnG+96AAdLo0VJeXsKHlZ6Ki6WffzZNVeWveVH+HZ6VJW21VUVTeuCrbVv+xVFDXKyKDDkKqIGiInMj8MUXzezPwY/US+aC101xmfhjM3JUYpCjorB4sbnwNX68uVhc05VlghUUmIth7drZr/btTZbiob6okaMiQ44CKvHHH9LVV5uHkyrTu7c0dKi545ZA5KjEIEeFqfymYXlD+qefRpebcnOl7bevmC29XTupTRvzatnSPPCHmCNHRYYcBd9ZulR6+WXTfB7qL0jJPFD0ySfm38ApiByVGOQoVay+98orZrXJ336LrLGqXIMGZlKoUK/Wrc3DsUgKclRkyFHIaD/8YK4pvf9+5fscd5x0551mpdU0QY5KDN/mqEWLpLffNvflJk+W1q2L/FjNm5tm9J12krbe2tyDK3/FcNJaRI8m9BhzHGc7SX/IBKjbXde9oZL9PpO0n0xI2cqt4X8sx3HukHRt2ea2rut6Hr91HGcbSeX1SscSKUIW4ubVV81sncGzeNapIz3zjHTCCTE/5XXXmVxYrl4980RaAie+8o0NG6SzzzbXN4MdeKA0bhyr00Vszhy7Kf3nn+1/OUSjVi3TiN6ypXm1aFHxfWCtSZMMemQzOlysqjlyFBAG15WmTjWN56+8Ym4aVubss6Unnoj7n8vkqMQhR8XAhg3STz9J331nXt9/b6b3iFVmchypVSu7Mb1dO2nLLc1qNE2bmhmvmBW0SuSomiNHASEsXCjdcov0+ONSSUnoffbeWxo+XNp338SOrQw5KnHIURFYv96sIPDee+Y1fXrsjp2dba4llTelB77atjVfmzZlQoQIkKNqjhwF39i40TSCPP+89M47ZqKbymyzjfk7IIVnHiRHJQ45KsiaNeaa0tSpZqbPr782//aIhbw8cx0puDm9ZUtzXalZMyY/iCNyVM2Ro5Cx/v1XuuEG6aWXKt9nn32ku++W9tsvceOKEXJU4vg+R23cKE2ZYv4d8sEH0l9/xe7YBQV2U3rwi9VnEiqeOcqv01j0lQlYkvRhFft9IBOy2kjaU9LUCM4jSbNDBSxJcl33H8dxZknaWubJw5iGLCAmCgvN0+Pl/0j/+uvQS5q1bWtmrOraNeZD2LhReuwxu3b66QSseMnPN1m9c2eT2wN98olZkW7SJGmHHZIzvrTWtq15nXyy2V692vw/Vd6UPnWqtHZtZMfetMkEwupCYW6u3aDeooV5OrGgQGrUyLwCv2/UyDyhyE1FGOQooDJ//GEaz196yVz8qk6PHtIjj8T9H9fkqMQiR8VAfr5ZTnyvvSpq69aZf5OUN6V/9535fy6SB+td11ytnT9f+vzzyvcrKKhoSq/qa5MmUsOGZtxcLEPVyFFAubVrpXvvNc3llc2006GDdNdd0vHHJ+3PV3JUYpGjIlCnjnTkkeYlmXwzbZr0yy/m9fPP0owZVTcxVqakxKzoN2+e9NVXoffJyzOzgTZvbjJRkyZS48YV34farlMn8l8v/IwchcxVWmr+bfr889Jrr0mrVlX/mb33Nn9ppnADOjkqschRQerXlw4+2Lwkcy1o7lxzP668Mf37781v1JoqLDT5asaMyvdp2LCiIb1586q/b9iQ+2+IN3IUMsvChWalvIcfNqsRh7L99maf445Ly2v25KjE8n2Oql1bOvpo85LMw3w//WSuL5W/fvut8v/fqrJihXn9+GPo9/PzTf9Us2amIb1JE/O1/BW83bBhWv4/7Qd+bUI/OOD7aVXs90PA94eoBiHLcZwtJW0XxjnKz7O1pI6O47RyXfe/cM8DxJzrSjNn2g3nP/xg/kFdlf32M49/NWsWl2G99pp3EtFBg+JyKpRxHOn6600+P/10e9L7f/4xPUGvvCIddVTyxpgRGjSQDj/cvCRzU/Dvv00j+Z9/VrxmzDBrAcVCUZG52DZ3bvifycoygS6wMb2gwNTq1TM3EOvWrXgFblf2fV4eATE9kaOAQAsWmEfjX3zR3LioTna2dMQR5i/Xfv0ScoOBHJV45Kg4qFvXzIAbOAvumjUVjenlzel//hndcsuByi+OVXVTMVB2tsl24b7q1ze/rvz8iledOvb3tWuTlzILOQr+tHKleUAv8DVhQuUzEW6xhXTTTdLAgUlflYIclXjkqChtuaV5de9eUdu0yeSZ8qb08gb1eTGY7KiwsOL/63DVrl3RmF5QYDJRvXoVr+Dtymr5+eZYOTnkJX8gRyHzzJhhGs9feEGaPbv6/Zs1k045Rerf3yx3n+J/9pGjEo8cVQXHqZgkqnw176Iik4mCJ2GL5OG9YKtWmVc4M4k6jsk6DRqYe27l142q+75+fe/1JK4lITRyFNJP+b81yx/6mTGjomdiyZLKP9eqlVlx78wzzb8V0xQ5KvHIUQHq1zc9gIErCBQWmtX3AhvTf/wx8gk2y23YUP3DfYGysysa05s0MX1LgdeN6te3v6+sVq+eORZiJn3/xI3OjmVf17iuW9Xj5IHdeZ0jPEfwccI5DyELieG6pqniu+8q/nH9zTdVh7ZQBgyQRo82DaVRDGXtWmnRoorXwoUV37/zjr3/wQebp9AQf337mlXljjvOTKZUbvVq6dhjpf33Nw/rlz+wH/y1WTPz9zfClJNjku3223vfW7MmdHP6n3+aH0g8lZZWNGLFiuOYJQkDX7VrV70dWDvrLHOxHYlGjoI/lJSYGwXLlknLl5tX8Pe//26WJystrf54e+0lnXqqaTyP8UN75KjURY6Ks/r1zX/E/fevqG3aZG7ez5xpv/7913xdvjx+4ykpiX1ekkz2CW5Qr13b/PurVi3ztfxV1Xbg90cfbZaKRqKRo5CZyh90Dm40L3+F++difr502WXSlVcmbGonclTqIkfFUK1a0s47m9epp1bUV6yoaEj/5Rdzjal80oLqJiSJxsaN0n//mVcsZGWZbBTuqzwP5eZ6v4ZT69YtpWcezmDkKKSHwkJzj23x4qpfixaFN0lM7dpSr16mE+bww1OqmYoclbrIUWHKzZV23dW8zj/f1IqLpTlzTLdZqFdlqzhFw3XND2f16tg8JCiZPzuCJzoInASh/BpR8HWk4Ffw+7m55s+h8lfwdvAr+P0mTcz5kWjkKKQm1zXhIbDBvPw1c6a53h6uBg2kq6+WLr44LVbfIkelLnJUJfLypK5dzeuss0yttNTko/Km9H/+MffnZs82v5HjoaSk4t9U0crJqbhWVH7fLZztUNeOqrquFPhednZFLir/PlQt1Pe1aqX06jmp8y/VBHEcp5akFmWb1f2OD3x/6xqeKnD/eJ4nof5583fNfWB8soeR+iKcec+Ru/nzjtwqv6psX6fs+6zSEuUUbVBO8QblFm0I+X1OUdl2+ffFESwxFmB9nSb65LDb9cNWA6Xh9hPVVf0n2LTJ/H0QGKIWLbKfJKvO4MERDhoR2W0383zCcceZZxbKlZaa5WeqU6eON3iV//2YlWV6kcu/r6pWXq9KVe+n/4P/9SXtal45kjqVvVxXddctVsHSv1R/zX+qt2aB6q5ZoHprFqjemoVlXxeozvqlVR494VzX3HiMZLlDSQs6HqSWNKEnFDkqOuSoMIUIEZszUhX7bc5Kbqmy3BLJLTXfl5bIKfvecUvklJba226pcoo3Kn/9ctXesFz5G5Yrf/0y1d64sixnRW7ZFtvpty6n6rcup2hFkw7SaklPVPrLrOyXZiFHpR9yVKLVkpl0p2zinYaSupa9JOVtXK1GK2aa1/J/1WjFTDVcMVONVsxS3bWLlL9hWdT/78dceV6KYXP7glHj1PICmtATiRwVHd/lqBhcV6rptuOWKLukUNklRcouKVRWSZGySos217IC3yut2Kf2hhVquGqOskprcGMwiOs4+mnXs/TZobdoTZ3W0qhq9idH+QY5Kt4KJB1gXm1lXpLkuqqzbokarJqrBivnmK9lr/rlX9f8F9X/9zFVWiqtX29eCWBy1PEJORcMclR0/JqjgnNPOLUst0ROabGySkuUVVqsrNJiOYHfuxXfZ5WW71us2htXqs7axaqzbonyN66M/pfgOJrd7iD92rW//ujcR4W1G5g5ZaubVzacY5OjfIMcFakcSe3LXoebuNSt7FV2L67Rsn9UsNy8GpV9bbhituqsW6zs0hjMoh4LUdx7i6cFo19Xy0G9kz0MXyFHRSctc1Q115RC3m8L+Kwjc5+tsh4lZ/PX0oDvXWUXb1JO8UblFG9Udskm5RRtrNguey+7uLy2SdnFG5VbvEHZJUVR/XKLs/P0/V4X6MuDrtMGt4l0X1SHqxI5yj/IUeHKkrStedXvZ92LyynaUHZdabYarpithisrXg1WzlaD1fOSf12puNg8DRLtbO4Jkuo5yndN6DIdfOWqS96Bf+TXr3SvJJ7HcZzW1ezSopr3a2TxlN900PvXxfKQSCN/qKO+1p6bXz+v31nFE3OliYkdR+vWUs+eiT0nzMpFn3xiHmp79dWafXb9emnWLPNCPDiSmpe9KperQjXXIrXQQrXUAuvVRMtUoBVqpJXWK1thzO6bJPOW1BLzTiUcOSoK5Ch/WKAWelkn6UWdqu+X7iZ96EgfJntUFchRyUGOSiUNJHUpe3llq1iNtVzNtFhNtaTSr+XfN1EcZ1aPo3mL88hRiUeOigI5KnO9raN1lXu3fv1+J+n7ZI+mauSo5CBHJYMjqVnZa7eQe2SpRC21QG00V601T1toqZpomZpomRpruef7Aq1I6WtMNUGOSgpyVBTIUenjV3XW8+qvl9xTNO/fNtK/kl5P9qhigxyVHOSoWAu8F7dPiPddFWiFmmmxmmmxmmuR5/vAWkPFeYXjFDRvYQ45KvHIUVEgR6WujaqlV3Sibiq5RbO/2Fr6Itkjih9yVHKQo6KVL2vCqCDZKlYr/aetNFutNU9NtExbaOnm60uB3zfVEuVX+1dL5kv1HOXHJvTA9X2qW1My8P2arteRqPOEsVYbUHNLtIXVcP6tdtdKFSR7WJKke+9NqRUHfaVOHenll81SP7fdZh4MQ/ooUp7mqY3mqU1Y+zsqVT2ttZrSQzWq19U61dU61dH6Kr/PU3RPMgcrya0V0+MhLOQoIIQ1qqfXdbxe0GmaooNVksL/zCJHJQ85Kj2UKEdL1ExL1Cys/XNUpIZapQZaXemrqvfztUH52qA6Wq98bVBWVTPRxFBpTl5CzgMLOQq+VqIszVUb/av2m18f6lB9oz2TPbSwkaOShxyVekqVrflqrfmqrgfEcFSqRlrpaVBvpJWqp7WbX/W1xtoOVUtUXqpMaXZuUs/vU+QoZKyFaq6XdIqeV3/9qK4yTa6ZhxyVPOSoRHK0Qo21Qo01Q9tXu3dtbVBTLVFDrbKuHYX6PlStvtYk9FpSLLjZ/EGQBOQopLVFaqYZ6rj59ae20wx11L9qr2L5499m5KjkIUfFT4lyNFdtNXfzsnxVy9f6kI3qDbR687Wj8utHwd+Xf62tTXH+VcWXm5Wd7CFUyY9/TAU+VVfdXdfA92u6lmOizgNEbZPyNE27aKr22tx0PlPtlKyLXXXqmGVJmjeXWrSo+L55c+mQQ6Ttq/93O+LIcaQbb5QGDJA+/tgsHbRokffrokVmaSGkL1dZWqMGWqMGYYe/quSoyGpKr62NqqVNm78GvoJroba32yK8ZnrEFDkKvrRBtbVMTbRcjbVcja3vv1M3vaVjtaHG10rjgxyV2shRmadYuVqmLbRMW8TgaK7yVGg1pQc3qZe/ammT8lS4+Wv5K9ztnIZNYzBe1BA5ChlvlRroH21jNZqXv+aorYqq/S2ZXOSo1EaOSm+usjY3Zf2jDlEdqY7Wq57Wbr4+VFsbla8Nm7+v6pWvDcpToXJVpFwVbf6+qlrweyX59WL23wVhI0ch7ZQoS0vUtGy+Y+9riZpqvrbUj+qa0pMZhIscldrIUalpo/LLmq+iUXEtKfj6UVXfl+ea4OtGldXK6zkqVq6KlKPiSl/l7+fK26lHE3pSkKOQ8jao9ubm8sBG8z+1nVapUbKHF3fkqNRGjkoNG1RH81Qn7Ak3Q8lR0eaG9PJ+pVDXjaqr1eQaUuDX8qyUrRLr+3BXDkz1HJXao4uPNQHf165m38Cn9dZUuldyz1Pd/10tJH1bw2NWqm6Hlvq6Bet8hMONuIHb2fx513EkOSG/N8ev2LfUyVZhdr4Ks/O1KTtfhVn5YW1vyq6jZbW3VHF2xYzCO5W9whqtU/V2qFp2ttS0qR2eAl/1uI6fFlq1kk45pfL3XVdasyZ0CFu61DwlWFpqv1y36lpJSeTjdSOcCCDSzyGUXEmNyl6hlcpcpdhQ6R4Vdg29IjTiixwVBXJU+ELnKG8teD/XyVKpkyVXWSp1sq3vXSdLJU62XGWV7VfxfnFWrtbmNtaavMZak9tEa/Maa01uY63JM98XZud7zh3o8Ah/neQo/yJHITRHUq2yV6OQe5RKWlf2isYNoVaNRryRo6LgxxwVi+tKkjZfT/LW7H1LnWwVZ+WpOCtXxU6eSrJyVezkVtSy8lTi5NrvZ+VpU3YdLa6ztRbWaa+1uQWe8JIvqXPZK5bIUf5FjvI7R1LdslflNpa94uGGg+N0YFSFHBWFdM1RkWchqfLs430v8P0SJ6fsepH5WuLkbK5t/pqVo1InRyUB+23IqaeVtZprVV4zraplXmtzC+Q6WVWOcsuyV6KRo/yLHJWJwruWtLbslVCuqyyVKqu0WNmueQ05LDUmcvEZclQUMjdHVf5+qZOl6nqUQu1TlFVLhdm1VZRVW4VZtVWUXdvUyr4PVSvKqqXVeVtoaX4bT25qWfZKNeQo/yJHZYJcSQVlL69Y3X+rKcctVZZboiy3ZHNmynJLNmeo8vp5B7dI8MhqxndN6K7rbnIcZ6FM+Gheze6B78+u4almVXKcmJ7Hdd15Vb3vhPobLwo7n7+vdP6EmB4TQGZyHKlBA/PqEM0ERwBSBjkqOuQoAOEiRwGZhxwVHXIUgHCRo4DMQ46KDjkKQLjIUYgtR1J22atWNfsiXshR0SFHAQgXOQqRyyp75SZ7IFGp+hHszPVb2df6juM0rGK/1iE+U9NzSNU/jRfNeQAAABKJHAUAABAZchQAAEBkyFE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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(5, 5, dpi=200, figsize=(15,15))\n", - "\n", - "for H0, a in zip(H0s, ax.flatten()):\n", - " ll = ac.get_slice_from_parameters(cube, [\"H0\", \"lmean\", \"lsigma\"], [H0, 2.16, .51], wanted=\"ll\")\n", - "\n", - " ll[np.isnan(ll)] = -1e99\n", - " ll -= np.max(ll)\n", - " ll = 10**ll\n", - " ll /= np.sum(ll)\n", - "\n", - " ll_real = ac.get_slice_from_parameters(cube_real, [\"H0\", \"lmean\", \"lsigma\"], [H0, 2.16, .51], wanted=\"ll\")\n", - " ll_real[np.isnan(ll_real)] = -1e99\n", - " ll_real -= np.max(ll_real)\n", - " ll_real = 10**ll_real\n", - " ll_real /= np.sum(ll_real)\n", - "\n", - " a.plot(cube[\"logF\"], ll, c=\"b\")\n", - " a.plot(cube[\"logF\"], ll_real, c=\"r\")\n", - " \n", - " a.set_xlabel(\"log F\")\n", - " a.set_ylabel(\"ll\")\n", - " a.text(.05, .925,f\"H_0 = {np.round(H0,3)}\", transform=a.transAxes)\n", - "\n", - "fig.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "H0_diag = 65\n", - "\n", - "lmeans = cube[\"lmean\"]\n", - "\n", - "fig, ax = plt.subplots(2, 5, dpi=200, figsize=(15,5))\n", - "\n", - "\n", - "for lmean, a in zip(lmeans, ax.flatten()):\n", - " ll = ac.get_slice_from_parameters(cube, [\"H0\", \"lmean\", \"lsigma\"], [H0_diag, lmean, .51], wanted=\"ll\")\n", - " ll[np.isnan(ll)] = -1e99\n", - " ll -= np.max(ll)\n", - " ll = 10**ll\n", - " ll /= np.sum(ll)\n", - "\n", - " ll_real = ac.get_slice_from_parameters(cube_real, [\"H0\", \"lmean\", \"lsigma\"], [H0_diag, lmean, .51], wanted=\"ll\")\n", - " ll_real[np.isnan(ll_real)] = -1e99\n", - " ll_real -= np.max(ll_real)\n", - " ll_real = 10**ll_real\n", - " ll_real /= np.sum(ll_real)\n", - "\n", - " a.plot(cube[\"logF\"], ll, c=\"b\", label=\"Synth\")\n", - " a.plot(cube[\"logF\"], ll_real, c=\"r\", label=\"Real\")\n", - " \n", - " a.set_xlabel(\"log F\")\n", - " a.set_ylabel(\"ll\")\n", - " a.text(.05, .925,f\"lmean = {np.round(lmean,3)}\", transform=a.transAxes)\n", - "\n", - " if lmean == lmeans[0]:\n", - " a.legend(loc=\"lower left\")\n", - "\n", - "fig.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "sigmas = cube[\"lsigma\"]\n", - "\n", - "fig, ax = plt.subplots(2, 5, dpi=200, figsize=(15,5))\n", - "\n", - "for sigma, a in zip(sigmas, ax.flatten()):\n", - " ll = ac.get_slice_from_parameters(cube, [\"H0\", \"lmean\", \"lsigma\"], [H0_diag, 2.16, sigma], wanted=\"ll\")\n", - " ll[np.isnan(ll)] = -1e99\n", - " ll -= np.max(ll)\n", - " ll = 10**ll\n", - " ll /= np.sum(ll)\n", - "\n", - " ll_real = ac.get_slice_from_parameters(cube_real, [\"H0\", \"lmean\", \"lsigma\"], [H0_diag, 2.16, sigma], wanted=\"ll\")\n", - " ll_real[np.isnan(ll_real)] = -1e99\n", - " ll_real -= np.max(ll_real)\n", - " ll_real = 10**ll_real\n", - " ll_real /= np.sum(ll_real)\n", - "\n", - " a.plot(cube[\"logF\"], ll, c=\"b\", label=\"Synth\")\n", - " a.plot(cube[\"logF\"], ll_real, c=\"r\", label=\"Real\")\n", - " \n", - " a.set_xlabel(\"log F\")\n", - " a.set_ylabel(\"ll\")\n", - " a.text(.05, .925,f\"lsigma = {np.round(sigma,3)}\", transform=a.transAxes)\n", - "\n", - " if sigma == sigmas[0]:\n", - " a.legend(loc=\"lower left\")\n", - "\n", - "fig.tight_layout()\n", - "plt.show()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## looking at the csvs" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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200 rows × 39 columns

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200 rows × 39 columns

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" - ], - "text/plain": [ - " n H0 lmean lsigma logF lC lls0 P_zDM0 \\\n", - "0 3000 61.0 1.700000 0.2 -1.700000 2.456119 -134.361007 -70.314131 \n", - "1 3001 61.0 1.788889 0.2 -1.700000 2.462146 -133.980656 -69.915665 \n", - "2 3002 61.0 1.877778 0.2 -1.700000 2.469533 -133.363349 -69.445574 \n", - "3 3003 61.0 1.966667 0.2 -1.700000 2.478569 -132.506835 -68.855895 \n", - "4 3004 61.0 2.055556 0.2 -1.700000 2.489590 -131.610451 -68.091086 \n", - ".. ... ... ... ... ... ... ... ... \n", - "195 3195 61.0 2.144444 0.9 -1.641379 2.558557 -122.300987 -58.979308 \n", - "196 3196 61.0 2.233333 0.9 -1.641379 2.572447 -122.158082 -58.778572 \n", - "197 3197 61.0 2.322222 0.9 -1.641379 2.587255 -122.059050 -58.623884 \n", - "198 3198 61.0 2.411111 0.9 -1.641379 2.602956 -122.004568 -58.515934 \n", - "199 3199 61.0 2.500000 0.9 -1.641379 2.619518 -121.995028 -58.455102 \n", - "\n", - " P_n0 P_s0 ... P_s4 N4 lls P_zDM P_n \\\n", - "0 -1.852750 -62.194126 ... -26.409137 10.135104 NaN NaN -6.093265 \n", - "1 -1.841901 -62.223089 ... -26.397414 10.252354 NaN NaN -6.055560 \n", - "2 -1.829250 -62.088525 ... -26.391494 10.397743 NaN NaN -6.011973 \n", - "3 -1.814655 -61.836284 ... -26.396009 10.578060 NaN NaN -5.962424 \n", - "4 -1.798024 -61.721341 ... -26.415383 10.801669 NaN NaN -5.907315 \n", - ".. ... ... ... ... ... ... ... ... \n", - "195 -1.786020 -61.535659 ... -27.028148 11.838436 NaN NaN -5.832236 \n", - "196 -1.776670 -61.602840 ... -27.076643 12.081667 NaN NaN -5.808666 \n", - "197 -1.766978 -61.668189 ... -27.125259 12.343646 NaN NaN -5.787282 \n", - "198 -1.756970 -61.731664 ... -27.173704 12.624716 NaN NaN -5.768678 \n", - "199 -1.746674 -61.793252 ... -27.221732 12.925142 NaN NaN -5.753456 \n", - "\n", - " P_s p_zgDM p_DM p_DMgz p_z \n", - "0 NaN -173.960800 -49.178061 -182.368963 -40.769898 \n", - "1 NaN -173.493922 -48.758558 -181.472093 -40.780387 \n", - "2 NaN -174.522314 -48.307544 -182.037088 -40.792770 \n", - "3 NaN -177.021467 -47.855724 -184.069796 -40.807395 \n", - "4 NaN -180.938682 -47.458513 -187.572490 -40.824705 \n", - ".. ... ... ... ... ... \n", - "195 NaN -120.280808 -47.391145 -126.753143 -40.918810 \n", - "196 NaN -120.565516 -47.354208 -126.977094 -40.942630 \n", - "197 NaN -120.875713 -47.343250 -127.250028 -40.968935 \n", - "198 NaN -121.209641 -47.359388 -127.571149 -40.997880 \n", - "199 NaN -121.565632 -47.403506 -127.939521 -41.029618 \n", - "\n", - "[200 rows x 39 columns]" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data = pd.read_csv(\"Cloud/Output/craco_real2.csv\")\n", - "data.iloc[np.where(np.isnan(data.lls))[0]]" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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10 rows × 39 columns

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" - ], - "text/plain": [ - " n H0 lmean lsigma logF lC lls0 P_zDM0 \\\n", - "150 150 60.0 1.700000 0.588889 -1.641379 2.477835 -124.372117 -61.415263 \n", - "151 151 60.0 1.788889 0.588889 -1.641379 2.486885 -123.636945 -60.558277 \n", - "152 152 60.0 1.877778 0.588889 -1.641379 2.497333 -123.000050 -59.801841 \n", - "153 153 60.0 1.966667 0.588889 -1.641379 2.509317 -122.472998 -59.159431 \n", - "154 154 60.0 2.055556 0.588889 -1.641379 2.522979 -122.064971 -58.641450 \n", - "155 155 60.0 2.144444 0.588889 -1.641379 2.538452 -121.783109 -58.255720 \n", - "156 156 60.0 2.233333 0.588889 -1.641379 2.555862 -121.632824 -58.007946 \n", - "157 157 60.0 2.322222 0.588889 -1.641379 2.575314 -121.618098 -57.902120 \n", - "158 158 60.0 2.411111 0.588889 -1.641379 2.596894 -121.741719 -57.940858 \n", - "159 159 60.0 2.500000 0.588889 -1.641379 2.620651 -122.005490 -58.125667 \n", - "\n", - " P_n0 P_s0 ... P_s4 N4 lls P_zDM P_n \\\n", - "150 -1.836145 -61.120709 ... -26.576688 10.564874 NaN NaN -6.010411 \n", - "151 -1.826085 -61.252583 ... -26.621327 10.734395 NaN NaN -5.973447 \n", - "152 -1.815199 -61.383011 ... -26.674749 10.931121 NaN NaN -5.934652 \n", - "153 -1.803515 -61.510052 ... -26.736668 11.158327 NaN NaN -5.894812 \n", - "154 -1.791067 -61.632454 ... -26.806292 11.419534 NaN NaN -5.854956 \n", - "155 -1.777898 -61.749490 ... -26.882364 11.718473 NaN NaN -5.816382 \n", - "156 -1.764053 -61.860826 ... -26.963274 12.059038 NaN NaN -5.780681 \n", - "157 -1.749581 -61.966397 ... -27.047233 12.445217 NaN NaN -5.749740 \n", - "158 -1.734534 -62.066327 ... -27.132476 12.880997 NaN NaN -5.725739 \n", - "159 -1.718966 -62.160858 ... -27.217432 13.370258 NaN NaN -5.711129 \n", - "\n", - " P_s p_zgDM p_DM p_DMgz p_z \n", - "150 NaN -130.552262 -48.037972 -137.681370 -40.908864 \n", - "151 NaN -131.027248 -47.699353 -137.803367 -40.923234 \n", - "152 NaN -131.633701 -47.394558 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "import zdm.analyze_cube as ac\n", - "import matplotlib.pyplot as plt\n", - "import zdm.analyze_cube as ac\n", - "\n", - "cube_dir_real = \"./Cubes/craco_real_cube.npz\"\n", - "cube_dir_full = \"../CRACO/Cubes/craco_full_cube.npz\"\n", - "\n", - "cube_real = np.load(cube_dir_real)\n", - "cube_full = np.load(cube_dir_full)\n", - "\n", - "lls_real = ac.get_slice_from_parameters(cube_real, [\"H0\", \"lsigma\"], [73, .51], verbose=False, wanted=\"ll\")\n", - "lls_full = ac.get_slice_from_parameters(cube_full, [\"H0\", \"lsigma\"], [73, .51], verbose=False, wanted=\"ll\")\n", - "\n", - "lls_real -= np.max(lls_real)\n", - "lls_real = 10**lls_real\n", - "lls_real /= np.sum(lls_real)\n", - "\n", - "lls_full -= np.max(lls_full)\n", - "lls_full = 10**lls_full\n", - "lls_full /= np.sum(lls_full)\n", - "\n", - "means, fs = np.meshgrid(cube_real[\"lmean\"], cube_real[\"logF\"])\n", - "\n", - "fig, ax = plt.subplots(1, 2, dpi=200, figsize=(12,5))\n", - "\n", - "f_full = ax[0].pcolormesh(means, fs, lls_full.T, shading=\"nearest\")\n", - "ax[0].set_xlabel(r\"$\\mathrm{DM_{host}}$\")\n", - "ax[0].set_ylabel(r\"$\\log_{10} F$\")\n", - "max_idx_i, max_idx_j = np.where(lls_full == lls_full.max())\n", - "ax[0].scatter(cube_real[\"lmean\"][max_idx_i], cube_real[\"logF\"][max_idx_j], c='red', marker='x', label=\"max\")\n", - "ax[0].legend()\n", - "ax[0].axhline(np.log10(0.32), c='k', ls='--', alpha=.25)\n", - "ax[0].axvline(2.16, c='k', ls='--', alpha=.25)\n", - "ax[0].set_title(\"CRACO Full Cube\")\n", - "plt.colorbar(f_full, label=r\"$\\log \\mathcal{L}$\", ax=ax[0])\n", - "\n", - "f_real = ax[1].pcolormesh(means, fs, lls_real.T, shading=\"nearest\")\n", - "ax[1].set_xlabel(r\"$\\mathrm{DM_{host}}$\")\n", - "ax[1].set_ylabel(r\"$\\log_{10} F$\")\n", - "max_idx_i, max_idx_j = np.where(lls_real == lls_real.max())\n", - "ax[1].scatter(cube_real[\"lmean\"][max_idx_i], cube_real[\"logF\"][max_idx_j], c='red', marker='x', label=\"max\")\n", - "ax[1].legend()\n", - "ax[1].axhline(np.log10(0.32), c='k', ls='--', alpha=.25)\n", - "ax[1].axvline(2.16, c='k', ls='--', alpha=.25)\n", - "ax[1].set_title(\"Real Cube\")\n", - "\n", - "fig.tight_layout()\n", - "plt.colorbar(f_real, label=r\"$\\log \\mathcal{L}$\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "for i in np.arange(len(cube_real[\"lsigma\"])):\n", - " plt.plot(cube_real[\"logF\"], lls_real[i, :])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.8.5 ('base')", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "9731a3b7ebb41545a410367c249afbc4842fd5740da82f1bd29e6ac1d7253e6e" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/papers/F/Analysis/Real/logF_sigma_comparison copy.ipynb b/papers/F/Analysis/Real/logF_sigma_comparison copy.ipynb deleted file mode 100644 index 799fd574..00000000 --- a/papers/F/Analysis/Real/logF_sigma_comparison copy.ipynb +++ /dev/null @@ -1,109 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "import zdm.analyze_cube as ac\n", - "import matplotlib.pyplot as plt\n", - "import zdm.analyze_cube as ac\n", - "\n", - "cube_dir_real = \"./Cubes/craco_real_cube.npz\"\n", - "cube_dir_full = \"../CRACO/Cubes/craco_full_cube.npz\"\n", - "\n", - "cube_real = np.load(cube_dir_real)\n", - "cube_full = np.load(cube_dir_full)\n", - "\n", - "lls_real = ac.get_slice_from_parameters(cube_real, [\"logF\", \"lmean\"], [-.49, 2.16], verbose=False, wanted=\"ll\")\n", - "lls_full = ac.get_slice_from_parameters(cube_full, [\"logF\", \"lmean\"], [-.49, 2.16], verbose=False, wanted=\"ll\")\n", - "\n", - "lls_real -= np.max(lls_real)\n", - "lls_real = 10**lls_real\n", - "lls_real /= np.sum(lls_real)\n", - "\n", - "lls_full -= np.max(lls_full)\n", - "lls_full = 10**lls_full\n", - "lls_full /= np.sum(lls_full)\n", - "\n", - "sigmas, H0s = np.meshgrid(cube_real[\"lsigma\"], cube_real[\"H0\"])\n", - "\n", - "fig, ax = plt.subplots(1, 2, dpi=200, figsize=(12,5))\n", - "\n", - "f_full = ax[0].pcolormesh(sigmas, H0s, lls_full, shading=\"nearest\")\n", - "ax[0].set_xlabel(r\"$\\sigma_\\mathrm{host}$\")\n", - "ax[0].set_ylabel(r\"$\\log_{10} F$\")\n", - "max_idx_i, max_idx_j = np.where(lls_full == lls_full.max())\n", - "ax[0].scatter(cube_real[\"lsigma\"][max_idx_i], cube_real[\"H0\"][max_idx_j], c='red', marker='x', label=\"max\")\n", - "ax[0].legend()\n", - "ax[0].axhline(64, c='k', ls='--', alpha=.25)\n", - "ax[0].axvline(0.51, c='k', ls='--', alpha=.25)\n", - "ax[0].set_title(\"CRACO Full Cube\")\n", - "plt.colorbar(f_full, label=r\"$\\log \\mathcal{L}$\", ax=ax[0])\n", - "\n", - "f_real = ax[1].pcolormesh(sigmas, H0s, lls_real, shading=\"nearest\")\n", - "ax[1].set_xlabel(r\"$\\sigma_\\mathrm{host}$\")\n", - "ax[1].set_ylabel(r\"$\\log_{10} F$\")\n", - "max_idx_i, max_idx_j = np.where(lls_real == lls_real.max())\n", - "ax[1].scatter(cube_real[\"lsigma\"][max_idx_i], cube_real[\"H0\"][max_idx_j], c='red', marker='x', label=\"max\")\n", - "ax[1].legend()\n", - "ax[1].axhline(64, c='k', ls='--', alpha=.25)\n", - "ax[1].axvline(0.51, c='k', ls='--', alpha=.25)\n", - "ax[1].set_title(\"Real Cube\")\n", - "\n", - "fig.tight_layout()\n", - "plt.colorbar(f_real, label=r\"$\\log \\mathcal{L}$\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.8.5 ('base')", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "9731a3b7ebb41545a410367c249afbc4842fd5740da82f1bd29e6ac1d7253e6e" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/papers/F/Analysis/Real/logF_sigma_comparison.ipynb b/papers/F/Analysis/Real/logF_sigma_comparison.ipynb deleted file mode 100644 index 1a10b562..00000000 --- a/papers/F/Analysis/Real/logF_sigma_comparison.ipynb +++ /dev/null @@ -1,132 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "import zdm.analyze_cube as ac\n", - "import matplotlib.pyplot as plt\n", - "import zdm.analyze_cube as ac\n", - "\n", - "cube_dir_real = \"./Cubes/craco_real_cube.npz\"\n", - "cube_dir_full = \"../CRACO/Cubes/craco_full_cube.npz\"\n", - "\n", - "cube_real = np.load(cube_dir_real)\n", - "cube_full = np.load(cube_dir_full)\n", - "\n", - "lls_real = ac.get_slice_from_parameters(cube_real, [\"H0\", \"lmean\"], [73, 2.16], verbose=False, wanted=\"ll\")\n", - "lls_full = ac.get_slice_from_parameters(cube_full, [\"H0\", \"lmean\"], [73, 2.16], verbose=False, wanted=\"ll\")\n", - "\n", - "lls_real -= np.max(lls_real)\n", - "lls_real = 10**lls_real\n", - "lls_real /= np.sum(lls_real)\n", - "\n", - "lls_full -= np.max(lls_full)\n", - "lls_full = 10**lls_full\n", - "lls_full /= np.sum(lls_full)\n", - "\n", - "sigmas, fs = np.meshgrid(cube_real[\"lsigma\"], cube_real[\"logF\"])\n", - "\n", - "fig, ax = plt.subplots(1, 2, dpi=200, figsize=(12,5))\n", - "\n", - "f_full = ax[0].pcolormesh(sigmas, fs, lls_full.T, shading=\"nearest\")\n", - "ax[0].set_xlabel(r\"$\\sigma_\\mathrm{host}$\")\n", - "ax[0].set_ylabel(r\"$\\log_{10} F$\")\n", - "max_idx_i, max_idx_j = np.where(lls_full == lls_full.max())\n", - "ax[0].scatter(cube_real[\"lsigma\"][max_idx_i], cube_real[\"logF\"][max_idx_j], c='red', marker='x', label=\"max\")\n", - "ax[0].legend()\n", - "ax[0].axhline(np.log10(0.32), c='k', ls='--', alpha=.25)\n", - "ax[0].axvline(0.51, c='k', ls='--', alpha=.25)\n", - "ax[0].set_title(\"CRACO Full Cube\")\n", - "plt.colorbar(f_full, label=r\"$\\log \\mathcal{L}$\", ax=ax[0])\n", - "\n", - "f_real = ax[1].pcolormesh(sigmas, fs, lls_real.T, shading=\"nearest\")\n", - "ax[1].set_xlabel(r\"$\\sigma_\\mathrm{host}$\")\n", - "ax[1].set_ylabel(r\"$\\log_{10} F$\")\n", - "max_idx_i, max_idx_j = np.where(lls_real == lls_real.max())\n", - "ax[1].scatter(cube_real[\"lsigma\"][max_idx_i], cube_real[\"logF\"][max_idx_j], c='red', marker='x', label=\"max\")\n", - "ax[1].legend()\n", - "ax[1].axhline(np.log10(0.32), c='k', ls='--', alpha=.25)\n", - "ax[1].axvline(0.51, c='k', ls='--', alpha=.25)\n", - "ax[1].set_title(\"Real Cube\")\n", - "\n", - "fig.tight_layout()\n", - "plt.colorbar(f_real, label=r\"$\\log \\mathcal{L}$\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "for i in np.arange(len(cube_real[\"lsigma\"])):\n", - " plt.plot(cube_real[\"logF\"], lls_real[i, :])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.8.5 ('base')", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "9731a3b7ebb41545a410367c249afbc4842fd5740da82f1bd29e6ac1d7253e6e" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/papers/F/Analysis/Real/make_fig10.py b/papers/F/Analysis/Real/make_fig10.py deleted file mode 100644 index 51fa62ba..00000000 --- a/papers/F/Analysis/Real/make_fig10.py +++ /dev/null @@ -1,59 +0,0 @@ -""" -Plots Figure 10 ('CRACO') analysis - -Produces plots for each parameter, even though only H0 -was shown in the paper. - -""" - -import numpy as np -import os -import zdm -from zdm import analyze_cube as ac - -from matplotlib import pyplot as plt - -def main(): - - if not os.path.exists("Figure10/"): - os.mkdir("Figure10") - - CubeFile='Cubes/craco_real_cube.npz' - if os.path.exists(CubeFile): - data=np.load(CubeFile) - else: - print("Missing cube file ",CubeFile," please download") - exit() - - data=np.load(CubeFile) - - lst = data.files - lldata=data["ll"] - params=data["params"] - # builds uvals list - uvals=[] - for param in params: - uvals.append(data[param]) - - deprecated,vectors,wvectors=ac.get_bayesian_data(data["ll"]) - - latexnames=[ - "H_0", - "\\mu_{\\rm host}", - "\\sigma_{\\rm host}", - "\\log_{10} F", - ] - units=[ - "km/s/Mpc", - "", - "", - "", - ] - - # ['[erg]','[km/s/Mpc]','','','','$[\\log_{10} {\\rm DM}]',''] - - truth=[67.66,2.16,.51,-0.49] - #ac.do_single_plots(uvals,vectors,wvectors,params,tag="prior_",truth=truth,dolevels=True,latexnames=latexnames) - ac.do_single_plots(uvals,vectors,None,params,tag="Figure10_",truth=truth,dolevels=True,latexnames=latexnames,units=units) - -main() diff --git a/papers/F/Analysis/Real/make_fig6.py b/papers/F/Analysis/Real/make_fig6.py deleted file mode 100644 index 9fa3a9d5..00000000 --- a/papers/F/Analysis/Real/make_fig6.py +++ /dev/null @@ -1,222 +0,0 @@ -""" -Makes figure 6 by fitting H0 outside the analysed range - -""" - -import numpy as np -import os -import zdm -from zdm import analyze_cube as ac - -from matplotlib import pyplot as plt -from scipy.optimize import curve_fit -from scipy.interpolate import interp1d -from scipy.integrate import quad - -from IPython import embed - -def main(): - - # saves values for H0 and marginalised p(H0) posteriors - if not (os.path.isfile("H0.npy") and os.path.isfile("pH0.npy") - and os.path.isfile("ph0_others_all_fixed.npy")): - get_H0_values() - - orig_H0=np.load("H0.npy") - orig_pH0=np.load("pH0.npy") - - # sets the range for fitting the tail of H0 - minH0=76 - maxH0=130 - OK=np.where(orig_H0>=minH0)[0] - H0=orig_H0[OK] - pH0=orig_pH0[OK] - - embed() - # lognormal fit - p0=[1.,1.,1.] - popt,pcov=curve_fit(ln,H0,pH0,p0=p0) - x=np.linspace(minH0,maxH0) - lnx=ln(x,*popt) - lnchisqr=np.sum((ln(H0,*popt)-pH0)**2) - - #spline interpolation - spl=interp1d(orig_H0,orig_pH0,kind='cubic') - longx=np.linspace(orig_H0[0],orig_H0[-1],100) - - # performs integration - p1=quad(spl,orig_H0[0],orig_H0[-1]) - #print("Integral over original range of using a spline comes to ",p1) - - p2=quad(ln,orig_H0[-1],maxH0+50.,args=(popt[0],popt[1],popt[2])) - #print("Integrating from ",orig_H0[-1]," to ",maxH0," gives an additional ",p2) - - # this figures shows a check where the lognormal fit is overplotted on the data - if not os.path.isdir('Figure6'): - os.makedirs("Figure6") - - plt.figure() - plt.scatter(orig_H0,orig_pH0,label="Data") - plt.scatter(H0,pH0, label="fitted data") - plt.plot(longx,spl(longx),label='Spline interpolation') - plt.plot(x,lnx,label="Lognormal extension",linestyle="--") - - plt.xlabel('$H_0$ [km/s/Mpc]') - plt.ylabel('$p(H_0)$') - plt.savefig("Figure6/check_fit.png", dpi=300) - plt.close() - - # renormalises data - p1 was original sum, p2 is new sum - norm=p1[0]+p2[0] - p1 = p1[0]/norm - p2 = p2[0]/norm - #print("Now ratios are ",p1,p2) - - # constructs single vector - nH=1000 #number of H0 points to sample at - xtotal=np.linspace(orig_H0[0],130.,nH) - lower=np.where(xtotal <= orig_H0[-1])[0] - upper=np.where(xtotal > orig_H0[-1])[0] - ytotal=np.zeros([nH]) - ytotal[lower]=spl(xtotal[lower])/norm - ytotal[upper]=ln(xtotal[upper],*popt)/norm - - # makes cumulative distribution - cy=np.cumsum(ytotal) - #print("Approx norm is ",cy[-1]*(xtotal[1]-xtotal[0])) - cy /= cy[-1] - - #orders data - asyt=np.argsort(ytotal) - syt=np.sort(ytotal) - csyt=np.cumsum(ytotal) - csyt /= csyt[-1] - - # values at which to calculate confidence levels - # (1-99.7)/2, (1-95)/2, (1-90)/2, (1-68)/2 (but to greater accuracy) - levels=np.array([0.00135,0.0228,0.05,0.15866]) - - labels=['99.7%','95%','90%','68%'] - linestyles=["--",":","-.","-"] - extrax=np.linspace(orig_H0[-1],maxH0,100) - - plt.figure() - plt.scatter(orig_H0,orig_pH0/norm,label="Data") - #plt.scatter(H0,pH0/norm, label="fitted data") - plt.plot(longx,spl(longx)/norm,label='Spline interpolation') - plt.plot(extrax,ln(extrax,*popt)/norm,label="Log-normal extension",linestyle="--") - - # gets pH0 when all other values fixed - other_H0=np.load('ph0_others_all_fixed.npy') - other_H0=other_H0[0] - spl=interp1d(orig_H0,other_H0,kind='cubic') - othery=spl(longx) - plt.plot(longx,othery,label="Fixed parameters",linestyle=":",color="black") - - plt.xlabel('$H_0$ [km/s/Mpc]') - plt.ylabel('$p(H_0)$') - - for i,l in enumerate(levels): - v1,v2,i1,i2=ac.extract_limits(xtotal,ytotal,l) - plt.plot([xtotal[i1],xtotal[i1]],[0.,ytotal[i1]],color="red",linestyle=linestyles[i]) - plt.plot([xtotal[i2],xtotal[i2]],[0.,ytotal[i2]],color="red",linestyle=linestyles[i]) - plt.text(xtotal[i1]-2,ytotal[i1]+1e-3,labels[i],rotation=90) - plt.text(xtotal[i2],ytotal[i2]+1e-3,labels[i],rotation=90) - - print("limits ",l,v1,v2) - - plt.gca().set_ylim(bottom=0) - plt.legend() - plt.tight_layout() - plt.savefig("Figure6/H0_fig6.png", dpi=300) - plt.close() - print("Wrote: Figure6/H0_fig6.png") - -def ln(x,*params): - a=params[0] - b=params[1] - c=params[2] - lnx=np.log(x) - vals=a*np.exp(-0.5*(lnx-b)**2/c)/x - return vals - -def exp(x,*params): - a=params[0] - b=params[1] - c=params[2] - vals = a*np.exp(-(x-c)/b) - return vals - -def get_H0_fixed_vales(): - - deprecated,uw_vectors,wvectors=ac.get_bayesian_data(data["ll"]) - - - -def get_H0_values(): - CubeFile='Cubes/craco_real_cube.npz' - if os.path.exists(CubeFile): - data=np.load(CubeFile) - else: - print("Could not file cube output file ",CubeFile) - print("Please obtain it from [repository]") - exit() - - lst = data.files - params=data["params"] - - param_vals = [] - param_list = [ - data["H0"], - data["lmean"], - data["lsigma"], - data["logF"] - ] - - for col in param_list: - unique = np.unique(col) - param_vals.append(unique) - - iH0=np.where(data["params"] == "H0") - ################ gets 1D H0 values ############ - deprecated,uw_vectors,wvectors=ac.get_bayesian_data(data["ll"]) - - print("H0: ",param_vals[iH0[0][0]]) - print("Probs: ",uw_vectors[iH0[0][0]]) - np.save("H0.npy",param_vals[iH0[0][0]]) - np.save("pH0.npy",uw_vectors[iH0[0][0]]) - - # builds uvals list, i.e. of unique values of parameters - uvals=[] - for ip,param in enumerate(data["params"]): - # switches for alpha - if param=="alpha": - uvals.append(data[param]*-1.) - else: - uvals.append(data[param]) - - # extract the best-fit parameter values - list2=[] - vals2=[] - for i,vec in enumerate(uw_vectors): - n=np.argmax(vec) - val=uvals[i][n] - if params[i] != "H0": - list2.append(params[i]) - vals2.append(val) - else: - iH0=i - - # gets the slice corresponding to the best-fit values of all other parameters - # this is 1D, so is our limit on H0 keeping all others fixed - pH0_fixed=ac.get_slice_from_parameters(data,list2,vals2) - - pH0_fixed -= np.max(pH0_fixed) - pH0_fixed = 10**pH0_fixed - pH0_fixed /= np.sum(pH0_fixed) - pH0_fixed /= (uvals[iH0][1]-uvals[iH0][0]) - - # saves this for generating special H0 plot - np.save("ph0_others_all_fixed.npy",[pH0_fixed]) - -main() diff --git a/papers/F/Analysis/Real/make_fig9.py b/papers/F/Analysis/Real/make_fig9.py deleted file mode 100644 index efb9d6e4..00000000 --- a/papers/F/Analysis/Real/make_fig9.py +++ /dev/null @@ -1,156 +0,0 @@ -""" -This is a script to produce figure 9 for the H0 paper -(James, Ghosh, Prochaska et al) - -It requires the original data "Cubefile", available at [repository] - -It outputs six plots, all being correlations between H0 -and the six other fitted parameters. The slices -obtained are set at the best-fit values of cube parameters. - -""" - -import numpy as np -import os -import zdm -from zdm import analyze_cube as ac - -from matplotlib import pyplot as plt - -def main(verbose=False): - - # output directory - opdir="Figure9/" - if not os.path.exists(opdir): - os.mkdir(opdir) - - CubeFile='Cubes/craco_real_cube.npz' - if os.path.exists(CubeFile): - data=np.load(CubeFile) - else: - print("Could not file cube output file ",CubeFile) - print("Please obtain it from [repository]") - exit() - - if verbose: - print("Data file contains the following items") - for thing in data: - print(thing) - - lst = data.files - lldata=data["ll"] - params=data["params"] - - def get_param_values(data,params): - """ - Gets the unique values of the data from a cube output - Currently the parameter order is hard-coded - - """ - param_vals=[] - for param in params: - col=data[param] - unique=np.unique(col) - param_vals.append(unique) - return param_vals - - param_vals=get_param_values(data, params) - - - #reconstructs total pdmz given all the pieces, including unlocalised contributions - pDMz=data["P_zDM0"]+data["P_zDM1"]+data["P_zDM2"]+data["P_zDM3"]+data["P_zDM4"] - - #DM only contribution - however it ignores unlocalised DMs from surveys 1-3 - pDMonly=data["pDM"]+data["P_zDM0"]+data["P_zDM4"] - - #do this over all surveys - P_s=data["P_s0"]+data["P_s1"]+data["P_s2"]+data["P_s3"]+data["P_s4"] - P_n=data["P_n0"]+data["P_n1"]+data["P_n2"]+data["P_n3"]+data["P_n4"] - - #labels=['p(N,s,DM,z)','P_n','P(s|DM,z)','p(DM,z)all','p(DM)all','p(z|DM)','p(DM)','p(DM|z)','p(z)'] - #for datatype in [data["ll"],P_n,P_s,pDMz,pDMonly,data["pzDM"],data["pDM"],data["pDMz"],data["pz"]]: - - # builds uvals list - uvals=[] - latexnames=[] - for ip,param in enumerate(data["params"]): - # switches for alpha - if param=="alpha": - uvals.append(data[param]*-1.) - else: - uvals.append(data[param]) - if param=="alpha": - latexnames.append('$\\alpha$') - ialpha=ip - elif param=="lEmax": - latexnames.append('$\\log_{10} E_{\\rm max}$') - elif param=="H0": - latexnames.append('$H_0$') - elif param=="gamma": - latexnames.append('$\\gamma$') - elif param=="sfr_n": - latexnames.append('$n_{\\rm sfr}$') - elif param=="lmean": - latexnames.append('$\\mu_{\\rm host}$') - elif param=="lsigma": - latexnames.append('$\\sigma_{\\rm host}$') - elif param=="logF": - latexnames.append('$\\log_{10} F$') - - #latexnames=['$\\log_{10} E_{\\rm max}$','$H_0$','$\\alpha$','$\\gamma$','$n_{\\rm sfr}$','$\\mu_{\\rm host}$','$\\sigma_{\\rm host}$'] - - list2=[] - vals2=[] - # gets Bayesian posteriors - deprecated,uw_vectors,wvectors=ac.get_bayesian_data(data["ll"]) - for i,vec in enumerate(uw_vectors): - n=np.argmax(vec) - val=uvals[i][n] - if params[i] != "logF": - list2.append(params[i]) - vals2.append(val) - else: - iF=i - - ###### NOTATION ##### - # uw: unweighted - # wH0: weighted according to H0 knowledged - # f: fixed other parameters - # B: best-fit - - ############## 2D plots at best-fit valuess ########## - - # gets the slice corresponding to the best-fit values of all other parameters - # this is 1D, so is our limit on H0 keeping all others fixed - for i,item in enumerate(list2): - - list3=np.concatenate((list2[0:i],list2[i+1:])) - vals3=np.concatenate((vals2[0:i],vals2[i+1:])) - array=ac.get_slice_from_parameters(data,list3,vals3) - - # log to lin space - array -= np.max(array) - array = 10**array - array /= np.sum(array) - - # now have array for slice covering best-fit values - if i < iF: - modi=i - else: - modi=i+1 - #array=array.T - array=array.swapaxes(0,1) - savename=opdir+"/lls_"+params[iF]+"_"+params[modi]+".png" - - if params[modi]=="alpha": - #switches order of array in alpha dimension - array=np.flip(array,axis=0) - ac.make_2d_plot(array,latexnames[modi],latexnames[iF], - -param_vals[modi],param_vals[iF], - savename=savename,norm=1) - else: - ac.make_2d_plot(array,latexnames[modi],latexnames[iF], - param_vals[modi],param_vals[iF], - savename=savename,norm=1) - -main() diff --git a/papers/F/Analysis/Real/make_ll_2D_F.py b/papers/F/Analysis/Real/make_ll_2D_F.py new file mode 100644 index 00000000..3c2939de --- /dev/null +++ b/papers/F/Analysis/Real/make_ll_2D_F.py @@ -0,0 +1,150 @@ +import numpy as np +import os +import zdm +from zdm import analyze_cube as ac + +from matplotlib import pyplot as plt +from IPython import embed + + +def main(verbose=False): + # output directory + opdir = "2d_figs/" + if not os.path.exists(opdir): + os.mkdir(opdir) + + CubeFile = "Cubes/craco_real_cube.npz" + if os.path.exists(CubeFile): + data = np.load(CubeFile) + else: + print("Could not file cube output file ", CubeFile) + print("Please obtain it from [repository]") + exit() + + if verbose: + print("Data file contains the following items") + for thing in data: + print(thing) + + lst = data.files + lldata = data["ll"] + params = data["params"] + + def get_param_values(data, params): + """ + Gets the unique values of the data from a cube output + Currently the parameter order is hard-coded + + """ + param_vals = [] + for param in params: + col = data[param] + unique = np.unique(col) + param_vals.append(unique) + return param_vals + + param_vals = get_param_values(data, params) + + # builds uvals list + uvals = [] + latexnames = [] + for ip, param in enumerate(data["params"]): + # switches for alpha + if param == "alpha": + uvals.append(data[param] * -1.0) + else: + uvals.append(data[param]) + if param == "alpha": + latexnames.append("$\\alpha$") + ialpha = ip + elif param == "lEmax": + latexnames.append("$\\log_{10} E_{\\rm max}$") + elif param == "H0": + latexnames.append("$H_0$") + elif param == "gamma": + latexnames.append("$\\gamma$") + elif param == "sfr_n": + latexnames.append("$n_{\\rm sfr}$") + elif param == "lmean": + latexnames.append("$\\mu_{\\rm host}$") + elif param == "lsigma": + latexnames.append("$\\sigma_{\\rm host}$") + elif param == "logF": + latexnames.append("$\\log_{10} F$") + + # latexnames=['$\\log_{10} E_{\\rm max}$','$H_0$','$\\alpha$','$\\gamma$','$n_{\\rm sfr}$','$\\mu_{\\rm host}$','$\\sigma_{\\rm host}$'] + + list2 = [] + vals2 = [] + # gets Bayesian posteriors + deprecated, uw_vectors, wvectors = ac.get_bayesian_data(data["ll"]) + for i, vec in enumerate(uw_vectors): + n = np.argmax(vec) + val = uvals[i][n] + if params[i] != "logF": + list2.append(params[i]) + vals2.append(val) + else: + iF = i + + ###### NOTATION ##### + # uw: unweighted + # wH0: weighted according to H0 knowledged + # f: fixed other parameters + # B: best-fit + + ############## 2D plots at best-fit valuess ########## + + # gets the slice corresponding to the best-fit values of all other parameters + # this is 1D, so is our limit on H0 keeping all others fixed + for i, item in enumerate(list2): + list3 = np.concatenate((list2[0:i], list2[i + 1 :])) + vals3 = np.concatenate((vals2[0:i], vals2[i + 1 :])) + array = ac.get_slice_from_parameters(data, list3, vals3) + + # log to lin space + array[np.isnan(array)] = -1e99 + array -= np.nanmax(array) + array = 10**array + array /= np.sum(array) + + # now have array for slice covering best-fit values + if i < iF: + modi = i + else: + modi = i + 1 + # array=array.T + array = array.swapaxes(0, 1) + savename = opdir + "/lls_" + params[iF] + "_" + params[modi] + ".png" + + # if (latexnames[modi] == '$\\gamma$'): + # embed(header="gamma") + + # if (latexnames[modi] == '$H_0$'): + # embed(header="H0") + + if params[modi] == "alpha": + # switches order of array in alpha dimension + array = np.flip(array, axis=0) + ac.make_2d_plot( + array, + latexnames[modi], + latexnames[iF], + -param_vals[modi], + param_vals[iF], + savename=savename, + norm=1, + ) + else: + ac.make_2d_plot( + array, + latexnames[modi], + latexnames[iF], + param_vals[modi], + param_vals[iF], + savename=savename, + norm=1, + ) + + +main() diff --git a/papers/F/Analysis/Real/test.py b/papers/F/Analysis/Real/make_ll_2D_H0.py similarity index 100% rename from papers/F/Analysis/Real/test.py rename to papers/F/Analysis/Real/make_ll_2D_H0.py diff --git a/papers/F/Analysis/Real/make_fig7.py b/papers/F/Analysis/Real/make_survey_contrib_fig.py similarity index 71% rename from papers/F/Analysis/Real/make_fig7.py rename to papers/F/Analysis/Real/make_survey_contrib_fig.py index ea1474e9..62c4157d 100644 --- a/papers/F/Analysis/Real/make_fig7.py +++ b/papers/F/Analysis/Real/make_survey_contrib_fig.py @@ -18,64 +18,83 @@ import scipy from IPython import embed + def main(verbose=False): - ######### other results #### Planck_H0 = 67.66 Planck_sigma = 0.5 Reiss_H0 = 73.04 Reiss_sigma = 1.42 - + # output directory - opdir="Figure7/" + opdir = "Figure7/" if not os.path.exists(opdir): os.mkdir(opdir) - - CubeFile='Cubes/craco_real_cube.npz' + + CubeFile = "Cubes/craco_real_cube.npz" if os.path.exists(CubeFile): - data=np.load(CubeFile) + data = np.load(CubeFile) else: - print("Could not file cube output file ",CubeFile) + print("Could not file cube output file ", CubeFile) print("Please obtain it from [repository]") exit() - + # builds uvals list - uvals=[] - latexnames=[] - for ip,param in enumerate(data["params"]): + uvals = [] + latexnames = [] + for ip, param in enumerate(data["params"]): # switches for alpha - if param=="alpha": - uvals.append(data[param]*-1.) + if param == "alpha": + uvals.append(data[param] * -1.0) else: uvals.append(data[param]) - if param=="alpha": - latexnames.append('$\\alpha$') - ialpha=ip - elif param=="lEmax": - latexnames.append('$\\log_{10} E_{\\rm max}$') - elif param=="H0": - latexnames.append('$H_0$') - elif param=="gamma": - latexnames.append('$\\gamma$') - elif param=="sfr_n": - latexnames.append('$n_{\\rm sfr}$') - elif param=="lmean": - latexnames.append('$\\mu_{\\rm host}$') - elif param=="lsigma": - latexnames.append('$\\sigma_{\\rm host}$') - elif param=="logF": - latexnames.append('$\\log_{10} F$') - + if param == "alpha": + latexnames.append("$\\alpha$") + ialpha = ip + elif param == "lEmax": + latexnames.append("$\\log_{10} E_{\\rm max}$") + elif param == "H0": + latexnames.append("$H_0$") + elif param == "gamma": + latexnames.append("$\\gamma$") + elif param == "sfr_n": + latexnames.append("$n_{\\rm sfr}$") + elif param == "lmean": + latexnames.append("$\\mu_{\\rm host}$") + elif param == "lsigma": + latexnames.append("$\\sigma_{\\rm host}$") + elif param == "logF": + latexnames.append("$\\log_{10} F$") + # 1D plots by surveys s only - contributions=[data["lls0"],data["lls2"],data["lls3"],data["lls1"],data["lls4"]] - labels=["CRAFT/FE","CRAFT/ICS 900 MHz","CRAFT/ICS 1.3 GHz","CRAFT/ICS 1.6 GHz","Parkes/Mb"] #correct - - colors=['blue','green','orange','purple','red'] - linestyles=['-',':','--','-','-.'] - make_1d_plots_by_contribution(data,contributions,labels,prefix="Figure7/by_survey_", - colors=colors,linestyles=linestyles)#,latexnames=latexnames) + contributions = [ + data["lls0"], + data["lls2"], + data["lls3"], + data["lls1"], + data["lls4"], + ] + labels = [ + "CRAFT/FE", + "CRAFT/ICS 900 MHz", + "CRAFT/ICS 1.3 GHz", + "CRAFT/ICS 1.6 GHz", + "Parkes/Mb", + ] # correct + + colors = ["blue", "green", "orange", "purple", "red"] + linestyles = ["-", ":", "--", "-", "-."] + make_1d_plots_by_contribution( + data, + contributions, + labels, + prefix="Figure7/by_survey_", + colors=colors, + linestyles=linestyles, + ) # ,latexnames=latexnames) exit() + def make_1d_plots_by_contribution( data, contributions, @@ -113,12 +132,7 @@ def make_1d_plots_by_contribution( # gets unique values for each axis param_vals = [] - param_list = [ - data["H0"], - data["lmean"], - data["lsigma"], - data["logF"] - ] + param_list = [data["H0"], data["lmean"], data["lsigma"], data["logF"]] xlatexnames = [ "H_0 {\\rm [km\,s^{-1}\,Mpc^{-1}]}", "\\mu_{\\rm host} {\\rm [pc\,cm^{-3}]}", @@ -141,7 +155,7 @@ def make_1d_plots_by_contribution( if colors is None: colors = plt.rcParams["axes.prop_cycle"].by_key()["color"] for which in np.arange(len(param_list)): - plt.figure() + plt.figure(dpi=300) plt.xlabel("$" + xlatexnames[which] + "$") plt.ylabel("$p(" + ylatexnames[which] + ")$") xvals = param_vals[which] @@ -183,4 +197,5 @@ def make_1d_plots_by_contribution( plt.savefig(prefix + params[which] + fig_exten, dpi=200) plt.close() + main() diff --git a/papers/F/Analysis/Real/py/craco_qck_explore.py b/papers/F/Analysis/Real/py/craco_qck_explore.py index ed1aa3bb..9050c7b5 100644 --- a/papers/F/Analysis/Real/py/craco_qck_explore.py +++ b/papers/F/Analysis/Real/py/craco_qck_explore.py @@ -41,6 +41,46 @@ def main(pargs): # Deconstruct the input_dict state_dict, cube_dict, vparam_dict = it.parse_input_dict(input_dict) + latexnames = [] + for ip, param in enumerate(npdict["params"]): + if param == "alpha": + latexnames.append("$\\alpha$") + ialpha = ip + elif param == "lEmax": + latexnames.append("$\\log_{10} E_{\\rm max}$") + elif param == "H0": + latexnames.append("$H_0$") + elif param == "gamma": + latexnames.append("$\\gamma$") + elif param == "sfr_n": + latexnames.append("$n_{\\rm sfr}$") + elif param == "lmean": + latexnames.append("$\\mu_{\\rm host}$") + elif param == "lsigma": + latexnames.append("$\\sigma_{\\rm host}$") + elif param == "logF": + latexnames.append("$\\log_{10} F$") + + units = [] + for ip, param in enumerate(npdict["params"]): + if param == "alpha": + units.append(" ") + ialpha = ip + elif param == "lEmax": + units.append("[$\rm erg$]") + elif param == "H0": + units.append(r"[$\rm km \, s^{-1} \, Mpc^{-1}$]") + elif param == "gamma": + units.append("") + elif param == "sfr_n": + units.append(" ") + elif param == "lmean": + units.append(r"[$\rm pc \, cm^{-3}$]") + elif param == "lsigma": + units.append(r"[$\rm pc \, cm^{-3}$]") + elif param == "logF": + units.append(" ") + # Run Bayes # Offset by max @@ -49,7 +89,16 @@ def main(pargs): uvals, vectors, wvectors = analyze_cube.get_bayesian_data(ll_cube) analyze_cube.do_single_plots( - uvals, vectors, wvectors, params, vparams_dict=vparam_dict, outdir=outdir + uvals, + vectors, + None, + params, + vparams_dict=vparam_dict, + outdir=outdir, + compact=True, + latexnames=latexnames, + units=units, + dolevels=True, ) print(f"Wrote figures to {outdir}") @@ -57,7 +106,7 @@ def main(pargs): def parse_option(): """ This is a function used to parse the arguments in the training. - + Returns: args: (dict) dictionary of the arguments. """ @@ -74,7 +123,6 @@ def parse_option(): # Command line execution if __name__ == "__main__": - pargs = parse_option() main(pargs) diff --git a/papers/F/Analysis/Real/testF.py b/papers/F/Analysis/Real/testF.py deleted file mode 100644 index a2804bf0..00000000 --- a/papers/F/Analysis/Real/testF.py +++ /dev/null @@ -1,138 +0,0 @@ -import numpy as np -import os -import zdm -from zdm import analyze_cube as ac - -from matplotlib import pyplot as plt -from IPython import embed - -def main(verbose=False): - - # output directory - opdir="2d_figs/" - if not os.path.exists(opdir): - os.mkdir(opdir) - - CubeFile='Cubes/craco_real_cube.npz' - if os.path.exists(CubeFile): - data=np.load(CubeFile) - else: - print("Could not file cube output file ",CubeFile) - print("Please obtain it from [repository]") - exit() - - if verbose: - print("Data file contains the following items") - for thing in data: - print(thing) - - lst = data.files - lldata=data["ll"] - params=data["params"] - - def get_param_values(data,params): - """ - Gets the unique values of the data from a cube output - Currently the parameter order is hard-coded - - """ - param_vals=[] - for param in params: - col=data[param] - unique=np.unique(col) - param_vals.append(unique) - return param_vals - - param_vals=get_param_values(data, params) - - # builds uvals list - uvals=[] - latexnames=[] - for ip,param in enumerate(data["params"]): - # switches for alpha - if param=="alpha": - uvals.append(data[param]*-1.) - else: - uvals.append(data[param]) - if param=="alpha": - latexnames.append('$\\alpha$') - ialpha=ip - elif param=="lEmax": - latexnames.append('$\\log_{10} E_{\\rm max}$') - elif param=="H0": - latexnames.append('$H_0$') - elif param=="gamma": - latexnames.append('$\\gamma$') - elif param=="sfr_n": - latexnames.append('$n_{\\rm sfr}$') - elif param=="lmean": - latexnames.append('$\\mu_{\\rm host}$') - elif param=="lsigma": - latexnames.append('$\\sigma_{\\rm host}$') - elif param=="logF": - latexnames.append('$\\log_{10} F$') - - #latexnames=['$\\log_{10} E_{\\rm max}$','$H_0$','$\\alpha$','$\\gamma$','$n_{\\rm sfr}$','$\\mu_{\\rm host}$','$\\sigma_{\\rm host}$'] - - list2=[] - vals2=[] - # gets Bayesian posteriors - deprecated,uw_vectors,wvectors=ac.get_bayesian_data(data["ll"]) - for i,vec in enumerate(uw_vectors): - n=np.argmax(vec) - val=uvals[i][n] - if params[i] != "logF": - list2.append(params[i]) - vals2.append(val) - else: - iF=i - - ###### NOTATION ##### - # uw: unweighted - # wH0: weighted according to H0 knowledged - # f: fixed other parameters - # B: best-fit - - ############## 2D plots at best-fit valuess ########## - - # gets the slice corresponding to the best-fit values of all other parameters - # this is 1D, so is our limit on H0 keeping all others fixed - for i,item in enumerate(list2): - - list3=np.concatenate((list2[0:i],list2[i+1:])) - vals3=np.concatenate((vals2[0:i],vals2[i+1:])) - array=ac.get_slice_from_parameters(data,list3,vals3) - - # log to lin space - array[np.isnan(array)] = -1e99 - array -= np.nanmax(array) - array = 10**array - array /= np.sum(array) - - # now have array for slice covering best-fit values - if i < iF: - modi=i - else: - modi=i+1 - #array=array.T - array=array.swapaxes(0,1) - savename=opdir+"/lls_"+params[iF]+"_"+params[modi]+".png" - -# if (latexnames[modi] == '$\\gamma$'): -# embed(header="gamma") - -# if (latexnames[modi] == '$H_0$'): -# embed(header="H0") - - if params[modi]=="alpha": - #switches order of array in alpha dimension - array=np.flip(array,axis=0) - ac.make_2d_plot(array,latexnames[modi],latexnames[iF], - -param_vals[modi],param_vals[iF], - savename=savename,norm=1) - else: - ac.make_2d_plot(array,latexnames[modi],latexnames[iF], - param_vals[modi],param_vals[iF], - savename=savename,norm=1) - -main() \ No newline at end of file diff --git a/papers/F/Analysis/Real/testing_bayesian.ipynb b/papers/F/Analysis/Real/testing_bayesian.ipynb deleted file mode 100644 index 914e2a08..00000000 --- a/papers/F/Analysis/Real/testing_bayesian.ipynb +++ /dev/null @@ -1,186 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import zdm.analyze_cube as ac" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "cube_dir = \"../CRACO/Cubes/craco_full_cube.npz\"\n", - "cube=np.load(cube_dir)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "lls = ac.get_slice_from_parameters(cube, [\"H0\", \"lmean\", \"lsigma\"], [70, 2.16, .51], wanted=\"ll\")\n", - "global_max = np.nanmax(lls)\n", - "lls -= global_max\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "NDIMS = len(lls.shape)\n", - "big_slice = [slice(None, None, None)] * NDIMS" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "test_lls = lls[tuple(big_slice)].flatten()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([-7.4237268e+02, -5.7061609e+02, -4.3890594e+02, -3.3762085e+02,\n", - " -2.5949152e+02, -1.9904089e+02, -1.5214069e+02, -1.1566675e+02,\n", - " -8.7230286e+01, -6.4988464e+01, -4.7544739e+01, -3.3898193e+01,\n", - " -2.3339355e+01, -1.5324219e+01, -9.4163818e+00, -5.2522583e+00,\n", - " -2.5090942e+00, -8.9019775e-01, -1.2945557e-01, 0.0000000e+00,\n", - " -3.1677246e-01, -9.3414307e-01, -1.7406006e+00, -2.6526489e+00,\n", - " -3.6091919e+00, -4.5662231e+00, -5.4932251e+00, -6.3695679e+00,\n", - " -7.1822510e+00, -7.9241333e+00], dtype=float32)" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "test_lls" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "ignore = np.where(test_lls == 0.0)[0]\n", - "test_lls[ignore] = -99999" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([-7.4237268e+02, -5.7061609e+02, -4.3890594e+02, -3.3762085e+02,\n", - " -2.5949152e+02, -1.9904089e+02, -1.5214069e+02, -1.1566675e+02,\n", - " -8.7230286e+01, -6.4988464e+01, -4.7544739e+01, -3.3898193e+01,\n", - " -2.3339355e+01, -1.5324219e+01, -9.4163818e+00, -5.2522583e+00,\n", - " -2.5090942e+00, -8.9019775e-01, -1.2945557e-01, -9.9999000e+04,\n", - " -3.1677246e-01, -9.3414307e-01, -1.7406006e+00, -2.6526489e+00,\n", - " -3.6091919e+00, -4.5662231e+00, -5.4932251e+00, -6.3695679e+00,\n", - " -7.1822510e+00, -7.9241333e+00], dtype=float32)" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "lls" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ True, True, True, True, True, True, True, True, True,\n", - " True, True, True, True, True, True, True, True, True,\n", - " True, True, True, True, True, True, True, True, True,\n", - " True, True, True])" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "test_lls == lls" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "themax = np.nanmax(lls)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "OKlls = np.isfinite(lls) & (lls > themax - 3)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "base", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "9731a3b7ebb41545a410367c249afbc4842fd5740da82f1bd29e6ac1d7253e6e" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/papers/F/Analysis/py/plotF_wH0Prior.py b/papers/F/Analysis/py/plotF_wH0Prior.py new file mode 100644 index 00000000..f4de09bb --- /dev/null +++ b/papers/F/Analysis/py/plotF_wH0Prior.py @@ -0,0 +1,238 @@ +""" +This is a script to produce limit plots for a cube + +It produces three sets of plots: +- single parameter limits with a prior on H0 between Planck and SN1a values +- single parameter limits also showing results with priors on H0 equal to: + a) Planck + b) Reiss + c) No prior +- 2D correlation plots with no prior onH0 + +It also collects data to plot a result on H0 for best-fit values of all +other parameters, but currently does not produce that plot + +""" + +import numpy as np +import os +import zdm +from zdm import analyze_cube as ac + +from matplotlib import pyplot as plt + + +def main(cube_path, outdir="./", verbose=False): + ######### sets the values of H0 for priors ##### + Planck_H0 = 67.4 + Planck_sigma = 0.5 + Reiss_H0 = 73.04 + Reiss_sigma = 1.42 + + ##### loads cube data ##### + data = np.load(cube_path) + if verbose: + for thing in data: + print(thing) + print(data["params"]) + + # gets values of cube parameters + # param_vals=get_param_values(data,verbose) + + # gets latex names + uvals, latexnames = get_names_values(data) + + ################ single plots, no priors ############ + deprecated, uw_vectors, wvectors = ac.get_bayesian_data(data["ll"]) + ac.do_single_plots( + uvals, + uw_vectors, + None, + data["params"], + tag="", + log=False, + logspline=False, + # kind="linear", + truth=None, + dolevels=True, + latexnames=latexnames, + outdir=outdir, + ) + + ########### H0 data for fixed values of other parameters ########### + # extracts best-fit values + list1 = [] + vals1 = [] + list2 = [] + vals2 = [] + vals3 = [] + for i, vec in enumerate(uw_vectors): + n = np.argmax(vec) # selects the most likely value + val = uvals[i][n] + if data["params"][i] == "H0": + # enables us to select a slice corresponding to particular H0 values + list1.append(data["params"][i]) + vals1.append(Reiss_H0) + + vals3.append(Planck_H0) + + iH0 = i # setting index for Hubble + else: + # enables us to select a slice correspondng to the best-fit values of all other params + # i.e. ignoring uncertainty in them + list2.append(data["params"][i]) + vals2.append(val) + + # gets the slice corresponding to specific values of H0 + Reiss_H0_selection = ac.get_slice_from_parameters(data, list1, vals1, verbose=True) + Planck_H0_selection = ac.get_slice_from_parameters(data, list1, vals3, verbose=True) + + # will have Bayesian limits on all parameters over everything but H0 + deprecated, ReissH0_vectors, deprecated = ac.get_bayesian_data(Reiss_H0_selection) + deprecated, PlanckH0_vectors, deprecated = ac.get_bayesian_data(Planck_H0_selection) + + # gets the slice corresponding to the best-fit values of all other parameters + # this is 1D, so is our limit on H0 keeping all others fixed + pH0_fixed = ac.get_slice_from_parameters(data, list2, vals2) + + ####### 1D plots for prior on H0 ######## + # generates plots for our standard prior on H0 only + # applies a prior on H0, which is flat between systematic differences, then falls off as a Gaussian either side + H0_dim = np.where(data["params"] == "H0")[0][0] + wlls = ac.apply_H0_prior( + data["ll"], H0_dim, data["H0"], Planck_H0, Planck_sigma, Reiss_H0, Reiss_sigma + ) + deprecated, wH0_vectors, wvectors = ac.get_bayesian_data(wlls) + ac.do_single_plots( + uvals, + wH0_vectors, + None, + data["params"], + tag="wH0_", + truth=None, + dolevels=True, + latexnames=latexnames, + logspline=False, + outdir=outdir, + ) + + # now do this with others... + # builds others... + others = [] + for i, p in enumerate(data["params"]): + if i == iH0: + oset = None + others.append(oset) + else: + if i < iH0: + modi = i + else: + modi = i - 1 + oset = [uw_vectors[i], ReissH0_vectors[modi], PlanckH0_vectors[modi]] + others.append(oset) + + # generates plots for our standard prior on H0, Planck and SN1a values, and no prior also + ac.do_single_plots( + uvals, + wH0_vectors, + None, + data["params"], + tag="wH0_others_", + truth=None, + dolevels=True, + latexnames=latexnames, + logspline=False, + others=others, + others_labels=["No Prior", r"$H_0 = 73.04$", r"$H_0 = 67.4$"], + outdir=outdir, + ) + + # ############## 2D plots for total likelihood ########### + # # these are for nor priors on anything + # baduvals, ijs, arrays, warrays = ac.get_2D_bayesian_data(data["ll"]) + # for which, array in enumerate(arrays): + # i = ijs[which][0] + # j = ijs[which][1] + + # savename = "2D/lls_" + data["params"][i] + "_" + data["params"][j] + ".png" + # ac.make_2d_plot( + # array, latexnames[i], latexnames[j], uvals[i], uvals[j], savename=savename + # ) + # # ac.make_2d_plot(array,latexnames[i],latexnames[j], + # # param_vals[i],param_vals[j], + # # savename=savename) + # if False and data["params"][i] == "H0": + # savename = ( + # "normed2D/lls_" + data["params"][j] + "_" + data["params"][i] + ".png" + # ) + + # ac.make_2d_plot( + # array.T, + # latexnames[j], + # latexnames[i], + # uvals[j], + # uvals[i], + # savename=savename, + # norm=1, + # ) + + +def get_names_values(data): + """ + Gets a list of latex names and corrected parameter values + """ + # builds uvals list + uvals = [] + latexnames = [] + for ip, param in enumerate(data["params"]): + # switches for alpha + if param == "alpha": + uvals.append(data[param] * -1.0) + else: + uvals.append(data[param]) + if param == "alpha": + latexnames.append("$\\alpha$") + ialpha = ip + elif param == "lEmax": + latexnames.append("$\\log_{10} E_{\\rm max}$") + elif param == "H0": + latexnames.append("$H_0$") + elif param == "gamma": + latexnames.append("$\\gamma$") + elif param == "sfr_n": + latexnames.append("$n_{\\rm sfr}$") + elif param == "lmean": + latexnames.append("$\\mu_{\\rm host}$") + elif param == "lsigma": + latexnames.append("$\\sigma_{\\rm host}$") + elif param == "logF": + latexnames.append("$\\log F$") + return uvals, latexnames + + +def get_param_values(data, verbose=False): + """ + Returns the unique cube values for each parameter in the cube + + Input: + data cube (tuple from reading the .npz) + + Output: + list of numpy arrays for each parameter giving their values + """ + # gets unique values for each axis + param_vals = [] + + # for col in param_list: + for col in data["params"]: + # unique=np.unique(col) + unique = np.unique(data[col]) + param_vals.append(unique) + if verbose: + print("For parameter ", col, " cube values are ", unique) + return param_vals + + +# Real Cube Data +main("../Real/Cubes/craco_real_cube.npz", "measured/") +main("../CRACO/Cubes/craco_full_cube.npz", "forecast/") diff --git a/papers/F/Analysis/py/plot_limits_from_cube.py b/papers/F/Analysis/py/plotHubble_wFPrior.py similarity index 93% rename from papers/F/Analysis/py/plot_limits_from_cube.py rename to papers/F/Analysis/py/plotHubble_wFPrior.py index 1ee97172..90593ba5 100644 --- a/papers/F/Analysis/py/plot_limits_from_cube.py +++ b/papers/F/Analysis/py/plotHubble_wFPrior.py @@ -19,16 +19,12 @@ from matplotlib import pyplot as plt -def main(verbose=False): +def main(cube_path, outdir="./", verbose=False): ######### sets the values of F for priors ##### F_0 = np.log10(0.32) F_sigma = np.abs(0.2 * F_0) # error of 20% on F - ##### loads cube data ##### - cube = "../Real/Cubes/craco_real_cube.npz" - # cube = "../CRACO/Cubes/craco_full_cube.npz" - - data = np.load(cube) + data = np.load(cube_path) if verbose: for thing in data: print(thing) @@ -54,6 +50,7 @@ def main(verbose=False): truth=None, dolevels=True, latexnames=latexnames, + outdir=outdir, ) ########### F data for fixed values of other parameters ########### @@ -102,6 +99,7 @@ def main(verbose=False): dolevels=True, latexnames=latexnames, logspline=False, + outdir=outdir, ) # now do this with others... @@ -131,6 +129,8 @@ def main(verbose=False): latexnames=latexnames, logspline=False, others=others, + outdir=outdir, + others_labels=["No prior", "Prior on $F$"], ) @@ -190,4 +190,6 @@ def get_param_values(data, verbose=False): return param_vals -main() +# Real Cube Data +# main("../Real/Cubes/craco_real_cube.npz", "H0_PriorOnF/") +main("../CRACO/Cubes/craco_full_cube.npz", "H0_PriorOnF/") diff --git a/papers/F/Figures/py/figs_compare.py b/papers/F/Figures/py/figs_compare.py index 675d85b6..5a510c09 100644 --- a/papers/F/Figures/py/figs_compare.py +++ b/papers/F/Figures/py/figs_compare.py @@ -32,8 +32,8 @@ def fig_varyF( ylim=None, iFRB=0, show_FRBs=True, + plotMacquart=True, ): - survey, grid = analy_F_I.craco_mc_survey_grid(iFRB=iFRB) fiducial_F = grid.state.IGM.logF @@ -41,7 +41,7 @@ def fig_varyF( fiducial_lmean = grid.state.host.lmean fiducial_lsigma = grid.state.host.lsigma - fig, ax = plt.subplots(dpi=200) + fig, ax = plt.subplots(dpi=300) ax.set_xlabel("z") ax.set_ylabel("${\\rm DM}_{\\rm EG}$") @@ -51,7 +51,6 @@ def fig_varyF( for F, H0, lmean, lsigma, lstyle, color in zip( Fs, H0s, lmeans, lsigmas, lstyles, lcolors ): - vparams = {} if F is None: @@ -112,9 +111,11 @@ def fig_varyF( Om0=grid.state.cosmo.Omega_m, ) - dms, zeval = figm.average_DM(3.0, cumul=True, cosmo=cosmo) + if plotMacquart: + dms, zeval = figm.average_DM(3.0, cumul=True, cosmo=cosmo) + l_mqr = ax.plot(f_z(zeval), f_DM(dms), ls="--", c=color, alpha=0.5) - l_mqr = ax.plot(f_z(zeval), f_DM(dms), ls="--", c=color, alpha=0.5) + print("F = ", F, "H0 = ", H0, "lmean = ", lmean, "lsigma = ", lsigma) # put down FRBs FRBZ = survey.frbs["Z"] @@ -143,18 +144,37 @@ def fig_varyF( print(f"Wrote: {outfile}") +# fig_varyF( +# "fig_varyF_H0_compare.png", +# Fs=[-0.57, -0.37, None], +# H0s=[69.02, 77.14, None], +# lmeans=[None, None, None], +# lsigmas=[None, None, None], +# lcolors=["r", "b", "k"], +# lstyles=["-", "-", "--"], +# labels=["Synthetic", "Real", "Fiducial"], +# DMmax=2500, +# Aconts=[0.01], +# show_FRBs=False, +# zmax=3, +# ) + +# 95th ptile fig_varyF( - "fig_varyF_H0_compare.png", - Fs=[-0.57, -0.37, None], - H0s=[69.02, 77.14, None], - lmeans=[2.21, 2.33, None], - lsigmas=[0.52, 0.53, None], - lcolors=["r", "b", "k"], - lstyles=["-", "-", "--"], - labels=["Synthetic", "Real", "Fiducial"], - DMmax=2500, - Aconts=[0.01], + "degeneracy/fig_H0_F_Degeneracy.png", + Fs=[None, np.log10(0.82)], + H0s=[None, 55], + lmeans=[None, None], + lsigmas=[None, None], + lcolors=["b", "r"], + lstyles=["-", "-"], + labels=[ + r"$H_0$ = 67.66, $\log_{10} F =$ -0.49", + r"$H_0$ = 55, $\log_{10} F =$ -0.086", + ], + DMmax=1800, + Aconts=[0.025], show_FRBs=False, - zmax=3, + zmax=2.3, + plotMacquart=False, ) - diff --git a/papers/F/Figures/py/figs_zdm_F_I.py b/papers/F/Figures/py/figs_zdm_F_I.py index f0e1ee9f..5d6d65ce 100644 --- a/papers/F/Figures/py/figs_zdm_F_I.py +++ b/papers/F/Figures/py/figs_zdm_F_I.py @@ -26,7 +26,7 @@ def fig_craco_varyF_zDM( fuss_with_ticks: bool = False, suppress_DM_host=False, iFRB=0, - show_FRBS=True + show_FRBS=True, ): """_summary_ @@ -47,7 +47,7 @@ def fig_craco_varyF_zDM( fiducial_lmean = grid.state.host.lmean fiducial_lsigma = grid.state.host.lsigma - plt.figure() + plt.figure(dpi=300) ax1 = plt.axes() plt.sca(ax1) @@ -78,7 +78,6 @@ def fig_craco_varyF_zDM( for F, scl, lstyle, clr in zip( F_values, other_values, lstyles, ["b", "k", "r", "gray"] ): - # Update grid vparams = {} vparams["logF"] = F @@ -228,9 +227,8 @@ def fig_varyF( zticks=None, ylim=None, iFRB=0, - show_FRBs=True + show_FRBs=True, ): - survey, grid = analy_F_I.craco_mc_survey_grid(iFRB=iFRB) fiducial_F = grid.state.IGM.logF @@ -248,7 +246,6 @@ def fig_varyF( labels = [] for F, other, lstyle, color in zip(F_values, other_values, lstyles, lcolors): - vparams = {} if F is None: @@ -403,10 +400,10 @@ def fig_craco_fiducial_F( H0=None, iFRB=0, suppress_DM_host=False, - show_FRBs=True + show_FRBs=True, ): """ - Very complicated routine for plotting 2D zdm grids + Very complicated routine for plotting 2D zdm grids Args: zDMgrid ([type]): [description] zvals ([type]): [description] @@ -496,11 +493,11 @@ def fig_craco_fiducial_F( ax = plt.gca() - ax.set_title(rf"$\log F = {F}$, $H_0$ = {H0}") + ax.set_title(rf"$\log_{{10}} F = {F}$, $H_0$ = {H0}") - muDMhost = np.log(10 ** grid.state.host.lmean) - sigmaDMhost = np.log(10 ** grid.state.host.lsigma) - meanHost = np.exp(muDMhost + sigmaDMhost ** 2 / 2.0) + muDMhost = np.log(10**grid.state.host.lmean) + sigmaDMhost = np.log(10**grid.state.host.lsigma) + meanHost = np.exp(muDMhost + sigmaDMhost**2 / 2.0) medianHost = np.exp(muDMhost) print(f"Host: mean={meanHost}, median={medianHost}") plt.ylim(0, ndm - 1) @@ -571,6 +568,7 @@ def fig_craco_fiducial_F( print(f"Wrote: {outfile}") plt.close() + ### tests # logfs = [-1.5, -1.5, -1.5] @@ -600,21 +598,33 @@ def fig_craco_fiducial_F( # ) fig_craco_fiducial_F( - f"figs/high_feedback_efficiency.png", + f"figs/fiducial_distribution.png", show_Macquart=True, + H0=None, + suppress_DM_host=False, + iFRB=100, + show_FRBs=True, + Aconts=[0.025], +) + +fig_craco_fiducial_F( + f"figs/high_feedback_efficiency.png", + show_Macquart=False, F=np.round(np.log10(0.01), 3), H0=None, suppress_DM_host=False, iFRB=100, - show_FRBs=False + show_FRBs=False, + Aconts=[0.025], ) fig_craco_fiducial_F( f"figs/low_feedback_efficiency.png", - show_Macquart=True, + show_Macquart=False, F=np.round(np.log10(0.9), 3), H0=None, suppress_DM_host=False, iFRB=100, - show_FRBs=False + show_FRBs=False, + Aconts=[0.025], ) diff --git a/papers/F/Tables/results.tex b/papers/F/Tables/results.tex new file mode 100644 index 00000000..1ce6f430 --- /dev/null +++ b/papers/F/Tables/results.tex @@ -0,0 +1,12 @@ +\newcommand{\Hubble}{\ensuremath{85.3_{-8.1}^{+9.4}}} +\newcommand{\fctH}{\ensuremath{69.2_{-4.9}^{+5.5}}} +\newcommand{\FnoPrior}{\ensuremath{ -0.75 _{-0.25}^{+0.33}}} +\newcommand{\fctFnoPrior}{\ensuremath{ -0.57 _{-0.16}^{+0.15}}} +\newcommand{\fctHwPrior}{\ensuremath{67.6_{-3.4}^{+3.5}}} +\newcommand{\FwPrior}{\ensuremath{ -0.48 _{-0.18}^{+0.26}}} +\newcommand{\FCMB}{\ensuremath{ -0.48 _{-0.18}^{+0.26}}} +\newcommand{\FSNe}{\ensuremath{ -0.48 _{-0.18}^{+0.26}}} +\newcommand{\fctFwPrior}{\ensuremath{ -0.60 _{-0.1}^{+0.09}}} +\newcommand{\fctFCMB}{\ensuremath{ -0.60 _{-0.1}^{+0.09}}} +\newcommand{\fctFSNe}{\ensuremath{ -0.48 _{-0.18}^{+0.26}}} +\newcommand{\Flower}{\ensuremath{-0.86}} diff --git a/papers/F/Tables/tab_frbs.tex b/papers/F/Tables/tab_frbs.tex new file mode 100644 index 00000000..e49d5618 --- /dev/null +++ b/papers/F/Tables/tab_frbs.tex @@ -0,0 +1,25 @@ +\begin{table*} +\centering +\begin{minipage}{170mm} +\centering +\caption{New FRB detections detected in 2022 used in addition to the FRB surveys used in \citet{j22b}. The FRB name, SNR-maximizing DM, \dmism\ estimated using the NE2001 model of \citet{CordesLazio01}, central frequency of observation $\nu$, measured signal-to-noise ratio SNR, redshift $z$, and original reference. Where redshifts are not given, this is because (a): no voltage data were dumped, preventing radio localization; (b) optical follow-up observations are not yet complete; (c) Substantial Galactic extinction has challenged follow-up optical observations; (d) the host galaxy appears too distant to accurately measure a redshift. All FRBs referenced are from Shannon et al. (in prep.) with the exception of FRB20220610A \citep{ryder22}. \label{tab:frbs}} +\begin{tabular}{ccccccc} +\hline +Name & Survey & DM & \dmism & $\nu$ & SNR & $z$ \\ +& & (\dmunits) & (\dmunits) & (MHz) & & +\\ +\hline +20220725A& \icslow& 290.4& 30.7& 920.5& 12.7& 0.1926\\ +20220501C& \icslow& 449.5& 30.6& 863.5& 16.1& 0.381\\ +20211203C& \icslow& 636.2& 63.4& 920.5& 14.2& 0.34386\\ +\hline +20220918A& \icsmid& 656.8& 40.7& 1271.5& 26.4& --\\ +20220610A& \icsmid& 1458.1& 31.0& 1271.5& 29.8& 1.016\\ +20220531A& \icsmid& 727.0& 70.0& 1271.5& 9.7& --\\ +\hline +20221106A& \icshigh& 344.0& 34.8& 1631.5& 35.1& --\\ +20220105A& \icshigh& 583.0& 22.0& 1632.5& 9.8& 0.2785\\ +\hline +\hline\end{tabular} +\end{minipage} +\end{table*} diff --git a/papers/F/Tables/tab_model_params.tex b/papers/F/Tables/tab_model_params.tex new file mode 100644 index 00000000..9d8a4e65 --- /dev/null +++ b/papers/F/Tables/tab_model_params.tex @@ -0,0 +1,22 @@ +\begin{deluxetable}{cccccc} +\tablewidth{20pt} +\tablecaption{z-DM grid parameters \label{tab:fullcube}} +\tabletypesize{\normalsize} +\tablehead{ \colhead{Parameter} & + \colhead{Unit} & + \colhead{Fiducial} & + \colhead{Min} & + \colhead{Max} & + \colhead{N} }\startdata +$H_0$ &km s$^{-1}$ Mpc$^{-1}$ &67.66 &60.00 &80.00 &21 \\ +$$\log_{10} F$$ & &-0.49 &-1.70 &0.00 &30 \\ +$\mu_{\rm host}$ &pc cm$^{-3}$ &2.18 &1.70 &2.50 &10 \\ +$\sigma_{\rm host}$ &pc cm$^{-3}$ &0.48 &0.20 &0.90 &10 \\ +$\alpha$ & &0.65 &-- &-- &-- \\ +$\gamma$ & &-1.01 &-- &-- &-- \\ +$n_{\rm sfr}$ & &0.73 &-- &-- &-- \\ +$\log_{10} E_{\rm max}$ &erg &41.40 &-- &-- &-- \\ +\hline +\enddata +\tablecomments{This table indicates the parameters of the high-resolution grid run. Non-degenerate parameters are held to the fiducial values. $N$ is the number of cells between the minimum and maximum parameter values.} +\end{deluxetable} diff --git a/papers/F/Tables/tables_F.py b/papers/F/Tables/tables_F.py new file mode 100644 index 00000000..bade8eda --- /dev/null +++ b/papers/F/Tables/tables_F.py @@ -0,0 +1,318 @@ +# Module for Tables for the Baptista+23 paper +# Imports +import numpy as np +import os, sys +import pandas + + +from zdm.craco import loading +from zdm import survey +from zdm import parameters +from zdm import misc_functions +from zdm import io +from zdm import iteration as it + +from IPython import embed + +# Local +sys.path.append(os.path.abspath("../Analysis/py")) +import analy_F_I +import pandas as pd + + +def mktab_model_params(outfile="tab_model_params.tex", sub=False): + isurvey, grid = analy_F_I.craco_mc_survey_grid() + tb = pd.read_json("../Analysis/CRACO/Cubes/craco_full_cube.json") + + # Open + tbfil = open(outfile, "w") + + # Header + tbfil.write("\\begin{deluxetable}{cccccc} \n") + tbfil.write("\\tablewidth{20pt} \n") + tbfil.write("\\tablecaption{z-DM grid parameters \label{tab:fullcube}} \n") + tbfil.write("\\tabletypesize{\\normalsize} \n") + tbfil.write( + "\\tablehead{ \colhead{Parameter} & \n \colhead{Unit} & \n \colhead{Fiducial} & \n \colhead{Min} & \n \colhead{Max} & \n \colhead{N} }" + ) + tbfil.write("\\startdata \n") + + params_vary = ["H0", "logF", "lmean", "lsigma"] + params_fix = ["alpha", "gamma", "sfr_n", "lEmax"] + + for key in params_vary: + item = grid.state.params[key] + latex = getattr(grid.state, item).meta(key)["Notation"] + min_val = tb[key]["min"] + max_val = tb[key]["max"] + n_val = tb[key]["n"] + + line = "" + # Name + line += f"${latex}$ &" + # Unit + line += f"{getattr(grid.state, item).meta(key)['unit']} &" + # Fiducial + line += f"{getattr(getattr(grid.state, item), key):.2f} &" + # Min + line += f"{min_val:.2f} &" + # Max + line += f"{max_val:.2f} &" + # N + line += f"{n_val} \\\\ \n" + tbfil.write(line) + + for key in params_fix: + item = grid.state.params[key] + latex = getattr(grid.state, item).meta(key)["Notation"] + + line = "" + # Name + line += f"${latex}$ &" + # Unit + line += f"{getattr(grid.state, item).meta(key)['unit']} &" + # Fiducial + line += f"{getattr(getattr(grid.state, item), key):.2f} &" + # Min + line += f"-- &" + # Max + line += f"-- &" + # N + line += f"-- \\\\ \n" + tbfil.write(line) + + tbfil.write("\\hline \n") + tbfil.write("\\enddata \n") + tbfil.write( + "\\tablecomments{This table indicates the parameters of the high-resolution grid run. Non-degenerate parameters are held to the fiducial values. $N$ is the number of cells between the minimum and maximum parameter values.} \n" + ) + tbfil.write("\\end{deluxetable} \n") + tbfil.close() + + print("Wrote {:s}".format(outfile)) + + +def mktex_measurements(outfile="results.tex"): + # Open + tbfil = open(outfile, "w") + + # Files where measurements are stored + craco_no_prior = "../Analysis/CRACO/logF_Full/limits.dat" + real_no_prior = "../Analysis/Real/real/limits.dat" + + real_F_wH0prior_file = "../Analysis/py/wH0_others_measured/limits.dat" + real_F_CMB_file = ( + "../Analysis/py/wH0_others_measured/limits_others_$H_0 = 67.4$.dat" + ) + real_F_SNe_file = ( + "../Analysis/py/wH0_others_measured/limits_others_$H_0 = 73.04$.dat" + ) + + craco_F_wH0prior_file = "../Analysis/py/wH0_others_forecast/limits.dat" + craco_F_CMB_file = ( + "../Analysis/py/wH0_others_forecast/limits_others_$H_0 = 67.4$.dat" + ) + craco_F_SNe_file = ( + "../Analysis/py/wH0_others_forecast/limits_others_$H_0 = 73.04$.dat" + ) + + real_H0_wFprior_file = "../Analysis/py/wF_H0_PriorOnF/limits.dat" + + def process_limits(lim, precision=1): + lower = str(round(float(lim[5:9]), precision)) + upper = str(round(float(lim[13:17]), precision)) + + return f"_{{-{lower}}}^{{+{upper}}}" + + # No Prior Measurements + + with open(craco_no_prior) as f: + craco_lines = f.readlines() + + with open(real_no_prior) as f: + real_lines = f.readlines() + + craco_H0 = str(round(float(craco_lines[0].split("&")[1]), 1)) + craco_H0_lim = process_limits(craco_lines[0].split("&")[-2]) + + ############### H0 with no prior ############### + real_H0 = str(round(float(real_lines[0].split("&")[1]), 1)) + real_H0_lim = process_limits(real_lines[0].split("&")[-2]) + + real_H0_tex = ( + f"\\newcommand{{\\Hubble}}{{\\ensuremath{{{real_H0}{real_H0_lim}}}}} \n" + ) + craco_H0_tex = ( + f"\\newcommand{{\\fctH}}{{\\ensuremath{{{craco_H0}{craco_H0_lim}}}}} \n" + ) + + tbfil.write(real_H0_tex) + tbfil.write(craco_H0_tex) + + ############### F with no prior ############### + craco_F = craco_lines[-1].split("&")[1] + craco_F_lim = process_limits(craco_lines[-1].split("&")[-2], 2) + + real_F = real_lines[-1].split("&")[1] + real_F_lim = process_limits(real_lines[-1].split("&")[-2], 2) + + real_F_tex = ( + f"\\newcommand{{\\FnoPrior}}{{\\ensuremath{{{real_F}{real_F_lim}}}}} \n" + ) + craco_F_tex = ( + f"\\newcommand{{\\fctFnoPrior}}{{\\ensuremath{{{craco_F}{craco_F_lim}}}}} \n" + ) + tbfil.write(real_F_tex) + tbfil.write(craco_F_tex) + + ############### H0 with F prior ############### + with open(real_H0_wFprior_file) as f: + real_H0_wFprior_lines = f.readlines() + + real_H0_wFprior = str(round(float(real_H0_wFprior_lines[0].split("&")[1]), 1)) + real_H0_wFprior_lim = process_limits(real_H0_wFprior_lines[0].split("&")[-2]) + real_H0_wFprior_tex = f"\\newcommand{{\\fctHwPrior}}{{\\ensuremath{{{real_H0_wFprior}{real_H0_wFprior_lim}}}}} \n" + + tbfil.write(real_H0_wFprior_tex) + + ############### F with H0 prior ############### + # Measurements + # Uniform Prior + with open(real_F_wH0prior_file) as f: + real_F_wH0prior_lines = f.readlines() + + real_F_wH0prior = real_F_wH0prior_lines[-1].split("&")[1] + real_F_wH0prior_lim = process_limits(real_F_wH0prior_lines[-1].split("&")[-2], 2) + + # CMB + with open(real_F_CMB_file) as f: + real_F_CMB_lines = f.readlines() + + real_F_CMB = real_F_CMB_lines[0].split("&")[1] + real_F_CMB_lim = process_limits(real_F_CMB_lines[0].split("&")[-2], 2) + + # SNe + with open(real_F_SNe_file) as f: + real_F_SNe_lines = f.readlines() + + real_F_SNe = real_F_SNe_lines[0].split("&")[1] + real_F_SNe_lim = process_limits(real_F_SNe_lines[0].split("&")[-2], 2) + + real_F_wH0prior_tex = f"\\newcommand{{\\FwPrior}}{{\\ensuremath{{{real_F_wH0prior}{real_F_wH0prior_lim}}}}} \n" + real_F_CMB_tex = ( + f"\\newcommand{{\\FCMB}}{{\\ensuremath{{{real_F_CMB}{real_F_CMB_lim}}}}} \n" + ) + real_F_SNe_tex = ( + f"\\newcommand{{\\FSNe}}{{\\ensuremath{{{real_F_SNe}{real_F_SNe_lim}}}}} \n" + ) + + tbfil.write(real_F_wH0prior_tex) + tbfil.write(real_F_CMB_tex) + tbfil.write(real_F_SNe_tex) + + # Forecasts + with open(craco_F_wH0prior_file) as f: + craco_F_wH0prior_lines = f.readlines() + + craco_F_wH0prior = craco_F_wH0prior_lines[-1].split("&")[1] + craco_F_wH0prior_lim = process_limits(craco_F_wH0prior_lines[-1].split("&")[-2], 2) + + with open(craco_F_CMB_file) as f: + craco_F_CMB_lines = f.readlines() + + craco_F_CMB = craco_F_CMB_lines[0].split("&")[1] + craco_F_CMB_lim = process_limits(craco_F_CMB_lines[0].split("&")[-2], 2) + + with open(craco_F_SNe_file) as f: + craco_F_SNe_lines = f.readlines() + + craco_F_SNe = real_F_SNe_lines[0].split("&")[1] + craco_F_SNe_lim = process_limits(real_F_SNe_lines[0].split("&")[-2], 2) + + craco_F_wH0prior_tex = f"\\newcommand{{\\fctFwPrior}}{{\\ensuremath{{{craco_F_wH0prior}{craco_F_wH0prior_lim}}}}} \n" + craco_F_CMB_tex = f"\\newcommand{{\\fctFCMB}}{{\\ensuremath{{{craco_F_CMB}{craco_F_CMB_lim}}}}} \n" + craco_F_SNe_tex = f"\\newcommand{{\\fctFSNe}}{{\\ensuremath{{{craco_F_SNe}{craco_F_SNe_lim}}}}} \n" + + tbfil.write(craco_F_wH0prior_tex) + tbfil.write(craco_F_CMB_tex) + tbfil.write(craco_F_SNe_tex) + + # Lower limit on F + arr = craco_F_wH0prior_lines[-1].split("&") + F_lower = str(float(arr[1]) - float(arr[2][5:9])) + F_lower_tex = f"\\newcommand{{\Flower}}{{\\ensuremath{{{F_lower}}}}} \n" + tbfil.write(F_lower_tex) + + tbfil.close() + + print("Wrote {:s}".format(outfile)) + + +def mktab_frbs(outfile="tab_frbs.tex", sub=False): + state = parameters.State() + zDMgrid, zvals, dmvals = misc_functions.get_zdm_grid( + state, new=True, plot=False, method="analytic" + ) + + # Load up the surveys + names = ["CRAFT/ICS892", "CRAFT/ICS", "CRAFT/ICS1632"] + survey_name = ["\icslow", "\icsmid", "\icshigh"] + new_frb_count = [3, 3, 2] + + # Open + tbfil = open(outfile, "w") + + # Header + tbfil.write("\\begin{table*}\n") + tbfil.write("\\centering\n") + tbfil.write("\\begin{minipage}{170mm} \n") + tbfil.write("\\centering\n") + tbfil.write( + "\\caption{New FRB detections detected in 2022 used in addition to the FRB surveys used in \citet{j22b}. The FRB name, SNR-maximizing DM, \dmism\ estimated using the NE2001 model of \citet{CordesLazio01}, central frequency of observation $\\nu$, measured signal-to-noise ratio SNR, redshift $z$, and original reference. Where redshifts are not given, this is because (a): no voltage data were dumped, preventing radio localization; (b) optical follow-up observations are not yet complete; (c) Substantial Galactic extinction has challenged follow-up optical observations; (d) the host galaxy appears too distant to accurately measure a redshift. All FRBs referenced are from Shannon et al. (in prep.) with the exception of FRB20220610A \citep{ryder22}. \label{tab:frbs}}\n" + ) + tbfil.write("\\begin{tabular}{ccccccc}\n") + tbfil.write("\\hline \n") + tbfil.write("Name & Survey & DM & \dmism & $\\nu$ & SNR & $z$ \\\ \n") + tbfil.write("& & (\dmunits) & (\dmunits) & (MHz) & & \n") + tbfil.write("\\\\ \n") + tbfil.write("\\hline \n") + + # Loop on survey + for i, name in enumerate(names): + isurvey = survey.load_survey(name, state, dmvals) + # Loop on FRBs + for frb_idx in range(new_frb_count[i]): + idx = int(-(frb_idx + 1)) + slin = f'{isurvey.frbs["TNS"].iat[idx]}' + slin += f"& {survey_name[i]}" + slin += f'& {isurvey.frbs["DM"].iat[idx]}' + slin += f'& {isurvey.frbs["DMG"].iat[idx]}' + slin += f'& {isurvey.frbs["FBAR"].iat[idx]}' + slin += f'& {isurvey.frbs["SNR"].iat[idx]}' + redshift = isurvey.frbs["Z"].iat[idx] + if redshift != -1: + slin += f"& {redshift}" + else: + slin += f"& --" + + # Write + tbfil.write(slin) + tbfil.write("\\\\ \n") + tbfil.write("\\hline \n") + + # End + tbfil.write("\\hline") + tbfil.write("\\end{tabular} \n") + tbfil.write("\\end{minipage} \n") + tbfil.write("\\end{table*} \n") + + tbfil.close() + + print("Wrote {:s}".format(outfile)) + + +# Command line execution +if __name__ == "__main__": + mktab_model_params() + mktex_measurements() + mktab_frbs() diff --git a/zdm/analyze_cube.py b/zdm/analyze_cube.py index 228c24dc..d6adac8f 100644 --- a/zdm/analyze_cube.py +++ b/zdm/analyze_cube.py @@ -893,7 +893,7 @@ def extract_limits(x,y,p,method=1): def do_single_plots(uvals,vectors,wvectors,names,tag=None, fig_exten='.png', dolevels=False,log=True,outdir='SingleFigs/', vparams_dict=None, prefix='',truth=None,latexnames=None, - units=None,logspline=True, others=None): + units=None,logspline=True, others=None, compact=False, others_labels=None): """ Generate a series of 1D plots of the cube parameters Args: @@ -946,8 +946,12 @@ def do_single_plots(uvals,vectors,wvectors,names,tag=None, fig_exten='.png', vparams_dict[names[i]]["min"], vparams_dict[names[i]]["max"], len(vals) ) - plt.figure() + plt.figure(dpi=300) lw = 3 + + if compact: + plt.gcf().set_figheight(3) + plt.gcf().set_figwidth(3.5) # get raw ylimits # removes zeroes, could lead to strange behaviour in theory @@ -976,7 +980,10 @@ def do_single_plots(uvals,vectors,wvectors,names,tag=None, fig_exten='.png', y /= norm vectors[i][temp] /= norm plt.plot(x, y, label="Uniform", color="blue", linewidth=lw, linestyle="-") - plt.plot(vals[temp], vectors[i][temp], color="blue", linestyle="", marker="s") + if compact: + plt.plot(vals[temp], vectors[i][temp], color="blue", linestyle="", marker="s", ms=5) + else: + plt.plot(vals[temp], vectors[i][temp], color="blue", linestyle="", marker="s") # weighted plotting if wvectors is not None: @@ -1227,12 +1234,34 @@ def do_single_plots(uvals,vectors,wvectors,names,tag=None, fig_exten='.png', norm = np.abs(norm) y /= norm + imax = np.argmax(y) + xmax = x[imax] + + if dolevels: + + logfile_others = outdir + f"limits_others_{others_labels[io]}.dat" + logfile_others = open(logfile_others, "w") + others_string = "" + # print(f"Confidence intervals for alternative distribution {names[i]}, {others_labels[io]}") + # limvals = np.array([0.00135, 0.0228, 0.05, 0.15866]) + # labels = ["99.7%", "95%", "90%", "68%"] + + limvals = np.array([0.15866]) + labels = ["68%"] + + for iav, av in enumerate(limvals): + v0, v1, ik1, ik2 = extract_limits(x, y, av, method=1) + others_string += "{} : ${}_{{{}}}^{{+{}}}$".format(labels[iav], format(xmax, '.2f'), format(v0 - xmax, '.2f'), format(v1 - xmax, '.2f')) + + logfile_others.write(string + "\n") + logfile_others.close() + # data_norm = np.sum(data) * (vals[1] - vals[0]) # data_norm = np.abs(norm) # plt.plot(vals, data/data_norm, color=other_colors[io % 3], marker="s") plt.plot( - x, y, color="grey", linewidth=1, linestyle=other_styles[io % 3] + x, y, color="grey", linewidth=1, linestyle=other_styles[io % 3], label=others_labels[io % 3] ) plt.legend() @@ -1419,7 +1448,7 @@ def make_2d_plot(array, xlabel, ylabel, xvals, yvals, savename=None, norm=None): savename (optional): string to save data under """ - plt.figure() + plt.figure(dpi=300) plt.xlabel(xlabel) plt.ylabel(ylabel) @@ -1454,11 +1483,11 @@ def make_2d_plot(array, xlabel, ylabel, xvals, yvals, savename=None, norm=None): clabel = "$p($" + xlabel + "," + ylabel + "$)$" plt.imshow(array.T, origin="lower", extent=extent, aspect=aspect) - plt.xlabel(xlabel) - plt.ylabel(ylabel) + plt.xlabel(xlabel, fontsize=18) + plt.ylabel(ylabel, fontsize=18) plt.xticks(rotation=90) cbar = plt.colorbar() - cbar.set_label(clabel) + cbar.set_label(clabel, fontsize=18) if savename is None: savename = xlabel + "_" + ylabel + ".pdf" plt.tight_layout() diff --git a/zdm/data/Surveys/test.ipynb b/zdm/data/Surveys/test.ipynb new file mode 100644 index 00000000..6479698c --- /dev/null +++ b/zdm/data/Surveys/test.ipynb @@ -0,0 +1,199 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "from astropy.table import unique, Table\n", + "import numpy as np\n", + "\n", + "surveys = [\n", + " \"CRAFT_class_I_and_II.ecsv\",\n", + " \"private_CRAFT_ICS_892.ecsv\",\n", + " \"private_CRAFT_ICS_1272.ecsv\",\n", + " \"private_CRAFT_ICS_1632.ecsv\",\n", + " \"parkes_mb_class_I_and_II.ecsv\"\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Z \n", + "---\n", + " --\n", + " --\n", + " --\n", + " --\n", + " --\n", + " --\n", + " --\n", + " --\n", + " --\n", + " --\n", + "...\n", + " --\n", + " --\n", + " --\n", + " --\n", + " --\n", + " --\n", + " --\n", + " --\n", + " --\n", + " --\n", + " --\n", + "Length = 26 rows\n", + " Z \n", + "-------\n", + " 0.23\n", + " 0.161\n", + " -1.0\n", + "0.36879\n", + " 0.28\n", + "0.12969\n", + " -1.0\n", + "0.34386\n", + " 0.381\n", + " 0.1926\n", + " Z \n", + "--------\n", + " 0.3214\n", + " 0.4755\n", + " 0.291\n", + " 0.1178\n", + " 0.378\n", + " 0.522\n", + " 0.209\n", + " 0.243\n", + " 0.214\n", + " -1.0\n", + " -1.0\n", + " -1.0\n", + "0.046946\n", + " -1.0\n", + " 1.016\n", + " -1.0\n", + " Z \n", + "------\n", + "0.0715\n", + "0.2785\n", + " -1.0\n", + " Z \n", + "---\n", + " --\n", + " --\n", + " --\n", + " --\n", + " --\n", + " --\n", + " --\n", + " --\n", + " --\n", + " --\n", + " --\n", + " --\n", + " --\n", + " --\n", + " --\n", + " --\n", + " --\n", + " --\n", + " --\n", + " --\n", + " --\n", + " --\n", + " --\n" + ] + } + ], + "source": [ + "survey_lengths = []\n", + "redshifts = []\n", + "for survey in surveys:\n", + " tb = Table.read(survey)\n", + " print(tb[\"Z\"])\n", + " if type(tb[\"Z\"].data) == np.ma.core.MaskedArray:\n", + " redshifts.append(tb['Z'].data.count())\n", + " else:\n", + " redshifts.append(sum(tb['Z'] != -1))\n", + " survey_lengths.append(len(Table.read(survey)))" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[0, 8, 11, 2, 0]" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "78" + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sum(survey_lengths)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/zdm/parameters.py b/zdm/parameters.py index c97bfb5b..2f9abfc2 100644 --- a/zdm/parameters.py +++ b/zdm/parameters.py @@ -11,236 +11,309 @@ # Analysis parameters @dataclass class AnalysisParams(data_class.myDataClass): - NewGrids: bool = field( - default=True, - metadata={'help': 'Generate new z, DM grids?'}) + NewGrids: bool = field(default=True, metadata={"help": "Generate new z, DM grids?"}) sprefix: str = field( - default='Std', - metadata={'help': 'Full: more detailed estimates. Takes more space and time \n'+\ - 'Std: faster - fine for max likelihood calculations, not as pretty'}) + default="Std", + metadata={ + "help": "Full: more detailed estimates. Takes more space and time \n" + + "Std: faster - fine for max likelihood calculations, not as pretty" + }, + ) + # Beam parameters @dataclass class BeamParams(data_class.myDataClass): Bmethod: int = field( default=2, - metadata={'help': 'Method for calculation. See beams.py:simplify_beam() for options', - 'unit': ''}) + metadata={ + "help": "Method for calculation. See beams.py:simplify_beam() for options", + "unit": "", + }, + ) Bthresh: float = field( default=0.0, - metadata={'help': 'Minimum value of beam sensitivity to consider', - 'unit': '', - 'Notation': 'B_{\rm min}'}) - #def __post_init__(self): + metadata={ + "help": "Minimum value of beam sensitivity to consider", + "unit": "", + "Notation": "B_{\rm min}", + }, + ) + # def __post_init__(self): # self.Nbeams = [5,5,5,10] + # Cosmology parameters @dataclass class CosmoParams(data_class.myDataClass): H0: float = field( default=Planck18.H0.value, - metadata={'help': "Hubble's constant", - 'unit': 'km s$^{-1}$ Mpc$^{-1}$', - 'Notation': 'H_0', - }) + metadata={ + "help": "Hubble's constant", + "unit": "km s$^{-1}$ Mpc$^{-1}$", + "Notation": "H_0", + }, + ) Omega_k: float = field( - default=0., - metadata={'help': 'photo density. Ignored here (we do not go back far enough)'}) + default=0.0, + metadata={"help": "photo density. Ignored here (we do not go back far enough)"}, + ) Omega_lambda: float = field( default=Planck18.Ode0, - metadata={'help': 'dark energy / cosmological constant (in current epoch)', - 'unit': '', - 'Notation': '\Omega_{\Lambda}', - }) + metadata={ + "help": "dark energy / cosmological constant (in current epoch)", + "unit": "", + "Notation": "\Omega_{\Lambda}", + }, + ) Omega_m: float = field( default=Planck18.Om0, - metadata={'help': 'matter density in current epoch', - 'unit': '', - 'Notation': '\Omega_m', - }) + metadata={ + "help": "matter density in current epoch", + "unit": "", + "Notation": "\Omega_m", + }, + ) Omega_b: float = field( default=Planck18.Ob0, - metadata={'help': 'baryon density in current epoch', - 'unit': '', - 'Notation': '\Omega_b', - }) + metadata={ + "help": "baryon density in current epoch", + "unit": "", + "Notation": "\Omega_b", + }, + ) Omega_b_h2: float = field( - default=Planck18.Ob0 * (Planck18.H0.value/100.)**2, - metadata={'help': 'baryon density weighted by $h_{100}^2$', - 'unit': '', - 'Notation': '\Omega_b h^2', - }) + default=Planck18.Ob0 * (Planck18.H0.value / 100.0) ** 2, + metadata={ + "help": "baryon density weighted by $h_{100}^2$", + "unit": "", + "Notation": "\Omega_b h^2", + }, + ) fix_Omega_b_h2: bool = field( - default=True, - metadata={'help': 'Fix Omega_b_h2 by the Placnk18 value?'}) + default=True, metadata={"help": "Fix Omega_b_h2 by the Placnk18 value?"} + ) + # FRB Demographics -- FRBdemo @dataclass class FRBDemoParams(data_class.myDataClass): source_evolution: int = field( default=0, - metadata={'help': 'Integer flag specifying the function used. '+\ - '0: SFR^n; 1: (1+z)^(2.7n)', - 'options': [0,1]}) + metadata={ + "help": "Integer flag specifying the function used. " + + "0: SFR^n; 1: (1+z)^(2.7n)", + "options": [0, 1], + }, + ) alpha_method: int = field( - default=0, - metadata={'help': 'Integer flag specifying the nature of scaling. '+\ - '0: spectral index interpretation: includes k-correction. Slower to update ' +\ - '1: rate interpretation: extra factor of (1+z)^alpha in source evolution', - 'options': [0,1]}) + default=0, + metadata={ + "help": "Integer flag specifying the nature of scaling. " + + "0: spectral index interpretation: includes k-correction. Slower to update " + + "1: rate interpretation: extra factor of (1+z)^alpha in source evolution", + "options": [0, 1], + }, + ) sfr_n: float = field( - default = 1.77, - metadata={'help': 'scaling of FRB rate density with star-formation rate', - 'unit': '', - 'Notation': 'n_{\\rm sfr}', - }) + default=1.77, + metadata={ + "help": "scaling of FRB rate density with star-formation rate", + "unit": "", + "Notation": "n_{\\rm sfr}", + }, + ) lC: float = field( - default = 4.19, - metadata={'help': 'log10 constant in number per Gpc^-3 yr^-1 at z=0'}) + default=4.19, + metadata={"help": "log10 constant in number per Gpc^-3 yr^-1 at z=0"}, + ) + # Galactic parameters @dataclass class MWParams(data_class.myDataClass): ISM: float = field( - default=35., - metadata={'help': 'Assumed DM for the Galactic ISM in units of pc/cm^3'}) + default=35.0, + metadata={"help": "Assumed DM for the Galactic ISM in units of pc/cm^3"}, + ) DMhalo: float = field( - default=50., - metadata={'help': 'DM for the Galactic halo', - 'unit': 'pc cm$^{-3}$', - 'Notation': '{\\rm DM}_{\\rm halo}', - }) + default=50.0, + metadata={ + "help": "DM for the Galactic halo", + "unit": "pc cm$^{-3}$", + "Notation": "{\\rm DM}_{\\rm halo}", + }, + ) + # Host parameters -- host @dataclass class HostParams(data_class.myDataClass): lmean: float = field( default=2.16, - metadata={'help': '$\log_{10}$ mean of DM host contribution in pc cm$^{-3}$', - 'unit': '', - 'Notation': '\mu_{\\rm host}', - }) + metadata={ + "help": "$\log_{10}$ mean of DM host contribution in pc cm$^{-3}$", + "unit": "pc cm$^{-3}$", + "Notation": "\mu_{\\rm host}", + }, + ) lsigma: float = field( default=0.51, - metadata={'help': '$\log_{10}$ sigma of DM host contribution in pc cm$^{-3}$', - 'unit': '', - 'Notation': '\sigma_{\\rm host}', - }) + metadata={ + "help": "$\log_{10}$ sigma of DM host contribution in pc cm$^{-3}$", + "unit": "pc cm$^{-3}$", + "Notation": "\sigma_{\\rm host}", + }, + ) + # IGM parameters @dataclass class IGMParams(data_class.myDataClass): logF: float = field( - default=0.32, - metadata={'help': 'logF parameter in DM$_{\\rm cosmic}$ PDF for the Cosmic web', - 'unit': '', - 'Notation': 'logF', - }) + default=np.log10(0.32), + metadata={ + "help": "logF parameter in DM$_{\\rm cosmic}$ PDF for the Cosmic web", + "unit": "", + "Notation": "$\log_{10} F$", + }, + ) # FRB intrinsic width parameters @dataclass class WidthParams(data_class.myDataClass): Wlogmean: float = field( - default = 1.70267, - metadata={'help': '$\log_{10}$ mean of intrinsic width distribution in ms', - 'unit': 'ms', - 'Notation': '\mu_{w}', - }) + default=1.70267, + metadata={ + "help": "$\log_{10}$ mean of intrinsic width distribution in ms", + "unit": "ms", + "Notation": "\mu_{w}", + }, + ) Wlogsigma: float = field( - default = 0.899148, - metadata={'help': '$\log_{10}$ sigma of intrinsic width distribution in ms', - 'unit': 'ms', - 'Notation': '\sigma_{w}', - }) + default=0.899148, + metadata={ + "help": "$\log_{10}$ sigma of intrinsic width distribution in ms", + "unit": "ms", + "Notation": "\sigma_{w}", + }, + ) Wthresh: int = field( default=0.5, - metadata={'help': 'Starting fraction of intrinsic width for histogramming', - 'unit': '', - 'Notation': 'w_{\\rm min}'}) + metadata={ + "help": "Starting fraction of intrinsic width for histogramming", + "unit": "", + "Notation": "w_{\\rm min}", + }, + ) Wmethod: int = field( default=2, - metadata={'help': 'Method of calculating FRB widths; 1 std, 2 includes scattering', - 'unit': ''}) + metadata={ + "help": "Method of calculating FRB widths; 1 std, 2 includes scattering", + "unit": "", + }, + ) Wbins: int = field( default=5, - metadata={'help': 'Number of bins for FRB width distribution', - 'unit': ''}) + metadata={"help": "Number of bins for FRB width distribution", "unit": ""}, + ) Wscale: int = field( default=3.5, - metadata={'help': 'Log-scaling of bins for width distribution', - 'unit': ''}) - + metadata={"help": "Log-scaling of bins for width distribution", "unit": ""}, + ) + + # FRB intrinsic scattering parameters @dataclass class ScatParams(data_class.myDataClass): Slogmean: float = field( - default = 0.7, - metadata={'help': 'Mean of log-scattering distribution at 600\,Mhz', - 'unit': 'ms', - 'Notation': '\log \mu_{s}', - }) + default=0.7, + metadata={ + "help": "Mean of log-scattering distribution at 600\,Mhz", + "unit": "ms", + "Notation": "\log \mu_{s}", + }, + ) Slogsigma: float = field( - default = 1.9, - metadata={'help': ' Standard deviation of log-scattering distribution at 600\,MHz ', - 'unit': 'ms', - 'Notation': '\log \sigma_{s}', - }) + default=1.9, + metadata={ + "help": " Standard deviation of log-scattering distribution at 600\,MHz ", + "unit": "ms", + "Notation": "\log \sigma_{s}", + }, + ) Sfnorm: float = field( - default = 600, - metadata={'help': 'Frequency of scattering width', - 'unit': 'MHz', - 'Notation': '\\nu_{\\tau}', - }) + default=600, + metadata={ + "help": "Frequency of scattering width", + "unit": "MHz", + "Notation": "\\nu_{\\tau}", + }, + ) Sfpower: float = field( - default = -4., - metadata={'help': 'Power-law scaling with frequency, nu^lambda', - 'unit': '', - 'Notation': '\lambda', - }) + default=-4.0, + metadata={ + "help": "Power-law scaling with frequency, nu^lambda", + "unit": "", + "Notation": "\lambda", + }, + ) + # FRB Energetics -- energy @dataclass class EnergeticsParams(data_class.myDataClass): lEmin: float = field( - default = 30., - metadata={'help': '$\log_{10}$ of minimum FRB energy ', - 'unit': 'erg', - 'Notation': '\\log_{10} E_{\\rm min}', - }) + default=30.0, + metadata={ + "help": "$\log_{10}$ of minimum FRB energy ", + "unit": "erg", + "Notation": "\\log_{10} E_{\\rm min}", + }, + ) lEmax: float = field( - default = 41.84, - metadata={'help': '$\log_{10}$ of maximum FRB energy', - 'unit': 'erg', - 'Notation': '\\log_{10} E_{\\rm max}', - }) + default=41.84, + metadata={ + "help": "$\log_{10}$ of maximum FRB energy", + "unit": "erg", + "Notation": "\\log_{10} E_{\\rm max}", + }, + ) alpha: float = field( - default = 1.54, - metadata={'help': 'power-law index of frequency dependent FRB rate, $R \sim \\nu^\\alpha$', - 'unit': '', - 'Notation': '\\alpha', - }) + default=1.54, + metadata={ + "help": "power-law index of frequency dependent FRB rate, $R \sim \\nu^\\alpha$", + "unit": "", + "Notation": "\\alpha", + }, + ) gamma: float = field( - default = -1.16, - metadata={'help': 'slope of luminosity distribution function', - 'unit': '', - 'Notation': '\gamma', - }) + default=-1.16, + metadata={ + "help": "slope of luminosity distribution function", + "unit": "", + "Notation": "\gamma", + }, + ) luminosity_function: int = field( - default = 2, - metadata={'help': 'luminosity function applied (0=power-law, 1=gamma, 2=spline+gamma, 3=gamma+linear+log10)'}) + default=2, + metadata={ + "help": "luminosity function applied (0=power-law, 1=gamma, 2=spline+gamma, 3=gamma+linear+log10)" + }, + ) + class State(data_class.myData): - """ Initialize the full state for the analysis + """Initialize the full state for the analysis with the default parameters """ - def __init__(self): + def __init__(self): self.set_dataclasses() self.set_params() - def set_dataclasses(self): self.scat = ScatParams() self.width = WidthParams() @@ -253,15 +326,15 @@ def set_dataclasses(self): self.IGM = IGMParams() self.energy = EnergeticsParams() - - def update_param(self, param:str, value): + def update_param(self, param: str, value): DC = self.params[param] setattr(self[DC], param, value) # Special treatment - if DC == 'cosmo' and param == 'H0': + if DC == "cosmo" and param == "H0": if self.cosmo.fix_Omega_b_h2: - self.cosmo.Omega_b = self.cosmo.Omega_b_h2/( - self.cosmo.H0/100.)**2 + self.cosmo.Omega_b = ( + self.cosmo.Omega_b_h2 / (self.cosmo.H0 / 100.0) ** 2 + ) def set_astropy_cosmo(self, cosmo): """Slurp the values from an astropy Cosmology object @@ -274,5 +347,5 @@ def set_astropy_cosmo(self, cosmo): self.cosmo.Omega_lambda = cosmo.Ode0 self.cosmo.Omega_m = cosmo.Om0 self.cosmo.Omega_b = cosmo.Ob0 - self.cosmo.Omega_b_h2 = cosmo.Ob0 * (cosmo.H0.value/100.)**2 + self.cosmo.Omega_b_h2 = cosmo.Ob0 * (cosmo.H0.value / 100.0) ** 2 return diff --git a/zdm/scripts/plot_limits_from_cube.py b/zdm/scripts/plot_limits_from_cube.py index 6fed7128..40dacd49 100644 --- a/zdm/scripts/plot_limits_from_cube.py +++ b/zdm/scripts/plot_limits_from_cube.py @@ -31,7 +31,6 @@ def main(verbose=False): ##### loads cube data ##### # cube = "../../papers/F/Analysis/Real/Cubes/craco_real_cube.npz" - # cube = "../../papers/F/Analysis/Real/Cubes/craco_real_old_cube.npz" cube = "../../papers/F/Analysis/CRACO/Cubes/craco_full_cube.npz" data = np.load(cube) if verbose: From cbfb1e6ad5351d47e8b2c8509abe99a808a6ef3e Mon Sep 17 00:00:00 2001 From: Jay Baptista Date: Wed, 5 Jul 2023 21:57:07 -1000 Subject: [PATCH 100/104] clean up the crud, document F files, new survey --- .vscode/settings.json | 6 - .../CRACO/Cloud/nautilus_craco_mini.yaml | 80 ------ .../CRACO/Cloud/nautilus_craco_tiny_logF.yaml | 80 ------ papers/F/Analysis/CRACO/Cloud/run.sh | 16 -- .../F/Analysis/CRACO/Cloud/run_craco_H0_F.py | 131 --------- .../Analysis/CRACO/Cloud/run_craco_H0_logF.py | 4 +- .../CRACO/Cloud/run_craco_full_logF.py | 4 +- .../F/Analysis/CRACO/Cloud/run_craco_lm_F.py | 131 --------- .../F/Analysis/CRACO/Cloud/run_craco_mini.py | 130 --------- .../CRACO/Cloud/run_craco_mini_logF.py | 4 +- papers/F/Analysis/CRACO/Contour/lower_CI.py | 258 ------------------ papers/F/Analysis/CRACO/Contour/pdelta.py | 56 ++-- papers/F/Analysis/CRACO/make_ll_2D_F.py | 4 + papers/F/Analysis/CRACO/make_ll_2D_H0.py | 138 ---------- .../F/Analysis/CRACO/py/craco_qck_explore.py | 16 +- papers/F/Analysis/CRACO/py/cube_test.ipynb | 134 --------- .../F/Analysis/CRACO/py/slurp_craco_cubes.py | 125 +-------- .../F/Analysis/Real/Cloud/run_craco_real.py | 5 +- .../Real/Cloud/run_real_craco_block.py | 5 + papers/F/Analysis/Real/Cloud/run_real_mini.py | 5 +- papers/F/Analysis/Real/make_ll_2D_F.py | 3 + papers/F/Analysis/Real/make_ll_2D_H0.py | 152 ----------- .../Analysis/Real/make_survey_contrib_fig.py | 9 +- .../F/Analysis/Real/py/craco_qck_explore.py | 6 + .../F/Analysis/Real/py/slurp_craco_cubes.py | 6 +- papers/F/Analysis/py/analy_F_I.py | 8 +- papers/F/Analysis/py/makeCornerPlot.ipynb | 243 +++++++++++++++++ papers/F/Analysis/py/plotF_wH0Prior.py | 15 +- .../Analysis/py/{get_PDFs.py => plotHWHM.py} | 21 +- papers/F/Analysis/py/plotHubble_wFPrior.py | 17 +- papers/F/Tables/results.tex | 9 + papers/F/Tables/tables_F.py | 78 +++++- zdm/analyze_cube.py | 7 +- zdm/craco/MC_F/Surveys/F_0.32_survey.dat | 1 + zdm/craco/MC_F/Surveys/F_0.32_survey.ecsv | 2 +- zdm/data/Surveys/CRAFT_ICS.ecsv | 3 + zdm/data/Surveys/CRAFT_ICS_1632.ecsv | 2 + zdm/data/Surveys/CRAFT_ICS_892.ecsv | 1 + zdm/survey.py | 6 +- 39 files changed, 454 insertions(+), 1467 deletions(-) delete mode 100644 .vscode/settings.json delete mode 100644 papers/F/Analysis/CRACO/Cloud/nautilus_craco_mini.yaml delete mode 100644 papers/F/Analysis/CRACO/Cloud/nautilus_craco_tiny_logF.yaml delete mode 100644 papers/F/Analysis/CRACO/Cloud/run.sh delete mode 100644 papers/F/Analysis/CRACO/Cloud/run_craco_H0_F.py delete mode 100644 papers/F/Analysis/CRACO/Cloud/run_craco_lm_F.py delete mode 100644 papers/F/Analysis/CRACO/Cloud/run_craco_mini.py delete mode 100644 papers/F/Analysis/CRACO/Contour/lower_CI.py delete mode 100644 papers/F/Analysis/CRACO/make_ll_2D_H0.py delete mode 100644 papers/F/Analysis/CRACO/py/cube_test.ipynb delete mode 100644 papers/F/Analysis/Real/make_ll_2D_H0.py create mode 100644 papers/F/Analysis/py/makeCornerPlot.ipynb rename papers/F/Analysis/py/{get_PDFs.py => plotHWHM.py} (91%) diff --git a/.vscode/settings.json b/.vscode/settings.json deleted file mode 100644 index 6ba1afd2..00000000 --- a/.vscode/settings.json +++ /dev/null @@ -1,6 +0,0 @@ -{ - "[python]": { - "editor.defaultFormatter": "ms-python.black-formatter" - }, - "python.formatting.provider": "none" -} \ No newline at end of file diff --git a/papers/F/Analysis/CRACO/Cloud/nautilus_craco_mini.yaml b/papers/F/Analysis/CRACO/Cloud/nautilus_craco_mini.yaml deleted file mode 100644 index 3d256586..00000000 --- a/papers/F/Analysis/CRACO/Cloud/nautilus_craco_mini.yaml +++ /dev/null @@ -1,80 +0,0 @@ -# 25 processors on mini for Varying F -# kubectl exec -it test-pod -- /bin/bash -apiVersion: batch/v1 -kind: Job -metadata: - name: xavier-zdm-craco-mini-f -spec: - backoffLimit: 0 - template: - spec: - affinity: - nodeAffinity: - requiredDuringSchedulingIgnoredDuringExecution: - nodeSelectorTerms: - - matchExpressions: - - key: kubernetes.io/hostname - operator: NotIn - values: - - k8s-chase-ci-01.noc.ucsb.edu - - key: nvidia.com/gpu.product - operator: In - values: - - NVIDIA-GeForce-GTX-1080-Ti - containers: - - name: container - image: localhost:30081/profxj/zdm_docker:latest # UPDATE - imagePullPolicy: Always - resources: - requests: - cpu: "25" - memory: "8Gi" # - ephemeral-storage: 50Gi # - limits: - cpu: "27" - memory: "12Gi" - ephemeral-storage: 100Gi - #nvidia.com/gpu: "1" # See docs to exlude certain types - command: ["/bin/bash", "-c"] - args: - - cd FRB; - git fetch; - git pull; - python setup.py develop; - cd ../ne2001; - python setup.py develop; - cd ../zdm; - git fetch; - git checkout varying_F; - python setup.py develop; - cd papers/F/Analysis/CRACO/Cloud; - python run_craco_mini.py -n 25 -t 25 -b 1; - aws --endpoint http://rook-ceph-rgw-nautiluss3.rook s3 cp Output s3://zdm/Cubes/F/mini/ --recursive --force; - env: - - name: "ENDPOINT_URL" - value: "http://rook-ceph-rgw-nautiluss3.rook" - - name: "S3_ENDPOINT" - value: "rook-ceph-rgw-nautiluss3.rook" - volumeMounts: - - name: prp-s3-credentials - mountPath: "/root/.aws/credentials" - subPath: "credentials" - - name: ephemeral - mountPath: "/tmp" - - name: "dshm" - mountPath: "/dev/shm" - nodeSelector: - nautilus.io/disktype: nvme - restartPolicy: Never - volumes: - # Secrets file for nautilus s3 credentials .aws/credentials and .s3cfg - - name: prp-s3-credentials - secret: - secretName: prp-s3-credentials - # Shared memory (necessary for Python's multiprocessing.shared_memory module to work) - - name: dshm - emptyDir: - medium: Memory - # Ephemeral storage - - name: ephemeral - emptyDir: {} diff --git a/papers/F/Analysis/CRACO/Cloud/nautilus_craco_tiny_logF.yaml b/papers/F/Analysis/CRACO/Cloud/nautilus_craco_tiny_logF.yaml deleted file mode 100644 index f9293ff3..00000000 --- a/papers/F/Analysis/CRACO/Cloud/nautilus_craco_tiny_logF.yaml +++ /dev/null @@ -1,80 +0,0 @@ -# 25 processors on mini for Varying F -# kubectl exec -it test-pod -- /bin/bash -apiVersion: batch/v1 -kind: Job -metadata: - name: jay-zdm-craco-tiny-logf -spec: - backoffLimit: 0 - template: - spec: - affinity: - nodeAffinity: - requiredDuringSchedulingIgnoredDuringExecution: - nodeSelectorTerms: - - matchExpressions: - - key: kubernetes.io/hostname - operator: NotIn - values: - - k8s-chase-ci-01.noc.ucsb.edu - - key: nvidia.com/gpu.product - operator: In - values: - - NVIDIA-GeForce-GTX-1080-Ti - containers: - - name: container - image: localhost:30081/profxj/zdm_docker:latest # UPDATE - imagePullPolicy: Always - resources: - requests: - cpu: "25" - memory: "8Gi" # - ephemeral-storage: 50Gi # - limits: - cpu: "27" - memory: "12Gi" - ephemeral-storage: 100Gi - #nvidia.com/gpu: "1" # See docs to exlude certain types - command: ["/bin/bash", "-c"] - args: - - cd FRB; - git fetch; - git pull; - python setup.py develop; - cd ../ne2001; - python setup.py develop; - cd ../zdm; - git fetch; - git checkout varying_F; - python setup.py develop; - cd papers/F/Analysis/CRACO/Cloud; - python run_craco_H0_logF.py -n 25 -t 25 -b 1; - aws --endpoint http://rook-ceph-rgw-nautiluss3.rook s3 cp Output s3://zdm/Cubes/F/full/ --recursive --force; - env: - - name: "ENDPOINT_URL" - value: "http://rook-ceph-rgw-nautiluss3.rook" - - name: "S3_ENDPOINT" - value: "rook-ceph-rgw-nautiluss3.rook" - volumeMounts: - - name: prp-s3-credentials - mountPath: "/root/.aws/credentials" - subPath: "credentials" - - name: ephemeral - mountPath: "/tmp" - - name: "dshm" - mountPath: "/dev/shm" - nodeSelector: - nautilus.io/disktype: nvme - restartPolicy: Never - volumes: - # Secrets file for nautilus s3 credentials .aws/credentials and .s3cfg - - name: prp-s3-credentials - secret: - secretName: prp-s3-credentials - # Shared memory (necessary for Python's multiprocessing.shared_memory module to work) - - name: dshm - emptyDir: - medium: Memory - # Ephemeral storage - - name: ephemeral - emptyDir: {} diff --git a/papers/F/Analysis/CRACO/Cloud/run.sh b/papers/F/Analysis/CRACO/Cloud/run.sh deleted file mode 100644 index 6b3683da..00000000 --- a/papers/F/Analysis/CRACO/Cloud/run.sh +++ /dev/null @@ -1,16 +0,0 @@ -#!/bin/bash - -#SBATCH --job-name=craco_mini # Job name -#SBATCH --partition=cpuq # queue for job submission -#SBATCH --account=cpuq # queue for job submission -#SBATCH --mail-type=ALL -#SBATCH --mail-user=jmbaptis@ucsc.edu -#SBATCH --nodes=1 -#SBATCH --ntasks=1 -#SBATCH --ntasks-per-node=1 -#SBATCH --time=24:00:00 -#SBATCH --output=craco_mini_%j.log - -module load python/3.8.6 - -python3.8 run_craco_mini.py -n 25 -t 25 -b 1 \ No newline at end of file diff --git a/papers/F/Analysis/CRACO/Cloud/run_craco_H0_F.py b/papers/F/Analysis/CRACO/Cloud/run_craco_H0_F.py deleted file mode 100644 index 7531f0af..00000000 --- a/papers/F/Analysis/CRACO/Cloud/run_craco_H0_F.py +++ /dev/null @@ -1,131 +0,0 @@ -""" Run a Nautilus test """ - -# It should be possible to remove all the matplotlib calls from this -# but in the current implementation it is not removed. -import argparse -import numpy as np -import os, sys -from pkg_resources import resource_filename - -from concurrent.futures import ProcessPoolExecutor -import subprocess - -from zdm import iteration as it -from zdm import io - -from IPython import embed - - -def main( - pargs, - pfile: str, - oproot: str, - NFRB: int = None, - iFRB: int = 0, - outdir: str = "Output", -): - - # Generate the folder? - if not os.path.isdir(outdir): - os.mkdir(outdir) - - ############## Load up ############## - input_dict = io.process_jfile(pfile) - - # Deconstruct the input_dict - state_dict, cube_dict, vparam_dict = it.parse_input_dict(input_dict) - - npoints = np.array([item["n"] for key, item in vparam_dict.items()]) - ntotal = int(np.prod(np.abs(npoints))) - - # Total number of CPUs to be running on this Cube - total_ncpu = pargs.ncpu if pargs.total_ncpu is None else pargs.total_ncpu - batch = 1 if pargs.batch is None else pargs.batch - - nper_cpu = ntotal // total_ncpu - if int(ntotal / total_ncpu) != nper_cpu: - raise IOError(f"Ncpu={total_ncpu} must divide evenly into ntotal={ntotal}") - - survey_file = os.path.join( - resource_filename("zdm", "craco"), "MC_F", "Surveys", "F_0.32_survey" - ) - commands = [] - for kk in range(pargs.ncpu): - line = [] - # Which CPU is running out of the total? - iCPU = (batch - 1) * pargs.ncpu + kk - outfile = os.path.join(outdir, oproot.replace(".csv", f"{iCPU+1}.csv")) - # Command - line = [ - "zdm_build_cube", - "-n", - f"{iCPU+1}", - "-m", - f"{nper_cpu}", - "-o", - f"{outfile}", - "-s", - f"{survey_file}", - "--clobber", - "-p", - f"{pfile}", - ] - # NFRB? - if NFRB is not None: - line += [f"--NFRB", f"{NFRB}"] - # iFRB? - if iFRB > 0: - line += [f"--iFRB", f"{iFRB}"] - # Finish - # line += ' & \n' - commands.append(line) - - # Launch em! - processes = [] - - for command in commands: - # Popen - print(f"Running this command: {' '.join(command)}") - pw = subprocess.Popen(command) - processes.append(pw) - - # Wait on em! - for pw in processes: - pw.wait() - - print("All done!") - - -def parse_option(): - # test for command-line arguments here - parser = argparse.ArgumentParser() - parser.add_argument( - "-n", - "--ncpu", - type=int, - required=True, - help="Number of CPUs to run on (might be split in batches)", - ) - parser.add_argument( - "-t", - "--total_ncpu", - type=int, - required=False, - help="Total number of CPUs to run on (might be split in batches)", - ) - parser.add_argument( - "-b", "--batch", type=int, default=1, required=False, help="Batch number" - ) - # parser.add_argument('--NFRB',type=int,required=False,help="Number of FRBs to analzye") - # parser.add_argument('--iFRB',type=int,default=0,help="Initial FRB to run from") - args = parser.parse_args() - - return args - - -if __name__ == "__main__": - # get the argument of training. - pfile = "../Cubes/craco_H0_F_cube.json" - oproot = "craco_H0_F.csv" - pargs = parse_option() - main(pargs, pfile, oproot, NFRB=100, iFRB=100) diff --git a/papers/F/Analysis/CRACO/Cloud/run_craco_H0_logF.py b/papers/F/Analysis/CRACO/Cloud/run_craco_H0_logF.py index e77b9c8b..8403c574 100644 --- a/papers/F/Analysis/CRACO/Cloud/run_craco_H0_logF.py +++ b/papers/F/Analysis/CRACO/Cloud/run_craco_H0_logF.py @@ -1,4 +1,6 @@ -""" Run a Nautilus test """ +""" +This script generates the `.csv` files for a 2D synthetic likelihood cube using the CRACO data (only H0 and log_{10}F). +""" # It should be possible to remove all the matplotlib calls from this # but in the current implementation it is not removed. diff --git a/papers/F/Analysis/CRACO/Cloud/run_craco_full_logF.py b/papers/F/Analysis/CRACO/Cloud/run_craco_full_logF.py index fbc185bd..7c4cdb3b 100644 --- a/papers/F/Analysis/CRACO/Cloud/run_craco_full_logF.py +++ b/papers/F/Analysis/CRACO/Cloud/run_craco_full_logF.py @@ -1,4 +1,6 @@ -""" Run a Nautilus test """ +""" +This script generates the `.csv` files for a synthetic likelihood cube using the CRACO data (see Baptista+23) +""" # It should be possible to remove all the matplotlib calls from this # but in the current implementation it is not removed. diff --git a/papers/F/Analysis/CRACO/Cloud/run_craco_lm_F.py b/papers/F/Analysis/CRACO/Cloud/run_craco_lm_F.py deleted file mode 100644 index 96690202..00000000 --- a/papers/F/Analysis/CRACO/Cloud/run_craco_lm_F.py +++ /dev/null @@ -1,131 +0,0 @@ -""" Run a Nautilus test """ - -# It should be possible to remove all the matplotlib calls from this -# but in the current implementation it is not removed. -import argparse -import numpy as np -import os, sys -from pkg_resources import resource_filename - -from concurrent.futures import ProcessPoolExecutor -import subprocess - -from zdm import iteration as it -from zdm import io - -from IPython import embed - - -def main( - pargs, - pfile: str, - oproot: str, - NFRB: int = None, - iFRB: int = 0, - outdir: str = "Output", -): - - # Generate the folder? - if not os.path.isdir(outdir): - os.mkdir(outdir) - - ############## Load up ############## - input_dict = io.process_jfile(pfile) - - # Deconstruct the input_dict - state_dict, cube_dict, vparam_dict = it.parse_input_dict(input_dict) - - npoints = np.array([item["n"] for key, item in vparam_dict.items()]) - ntotal = int(np.prod(np.abs(npoints))) - - # Total number of CPUs to be running on this Cube - total_ncpu = pargs.ncpu if pargs.total_ncpu is None else pargs.total_ncpu - batch = 1 if pargs.batch is None else pargs.batch - - nper_cpu = ntotal // total_ncpu - if int(ntotal / total_ncpu) != nper_cpu: - raise IOError(f"Ncpu={total_ncpu} must divide evenly into ntotal={ntotal}") - - survey_file = os.path.join( - resource_filename("zdm", "craco"), "MC_F", "Surveys", "F_0.32_survey" - ) - commands = [] - for kk in range(pargs.ncpu): - line = [] - # Which CPU is running out of the total? - iCPU = (batch - 1) * pargs.ncpu + kk - outfile = os.path.join(outdir, oproot.replace(".csv", f"{iCPU+1}.csv")) - # Command - line = [ - "zdm_build_cube", - "-n", - f"{iCPU+1}", - "-m", - f"{nper_cpu}", - "-o", - f"{outfile}", - "-s", - f"{survey_file}", - "--clobber", - "-p", - f"{pfile}", - ] - # NFRB? - if NFRB is not None: - line += [f"--NFRB", f"{NFRB}"] - # iFRB? - if iFRB > 0: - line += [f"--iFRB", f"{iFRB}"] - # Finish - # line += ' & \n' - commands.append(line) - - # Launch em! - processes = [] - - for command in commands: - # Popen - print(f"Running this command: {' '.join(command)}") - pw = subprocess.Popen(command) - processes.append(pw) - - # Wait on em! - for pw in processes: - pw.wait() - - print("All done!") - - -def parse_option(): - # test for command-line arguments here - parser = argparse.ArgumentParser() - parser.add_argument( - "-n", - "--ncpu", - type=int, - required=True, - help="Number of CPUs to run on (might be split in batches)", - ) - parser.add_argument( - "-t", - "--total_ncpu", - type=int, - required=False, - help="Total number of CPUs to run on (might be split in batches)", - ) - parser.add_argument( - "-b", "--batch", type=int, default=1, required=False, help="Batch number" - ) - # parser.add_argument('--NFRB',type=int,required=False,help="Number of FRBs to analzye") - # parser.add_argument('--iFRB',type=int,default=0,help="Initial FRB to run from") - args = parser.parse_args() - - return args - - -if __name__ == "__main__": - # get the argument of training. - pfile = "../Cubes/craco_lm_F_cube.json" - oproot = "craco_lm_F.csv" - pargs = parse_option() - main(pargs, pfile, oproot, NFRB=100, iFRB=100) diff --git a/papers/F/Analysis/CRACO/Cloud/run_craco_mini.py b/papers/F/Analysis/CRACO/Cloud/run_craco_mini.py deleted file mode 100644 index 0239dbb4..00000000 --- a/papers/F/Analysis/CRACO/Cloud/run_craco_mini.py +++ /dev/null @@ -1,130 +0,0 @@ -""" Run a Nautilus test """ - -# It should be possible to remove all the matplotlib calls from this -# but in the current implementation it is not removed. -import argparse -import numpy as np -import os, sys -from pkg_resources import resource_filename - -from concurrent.futures import ProcessPoolExecutor -import subprocess - -from zdm import iteration as it -from zdm import io - -from IPython import embed - - -def main( - pargs, - pfile: str, - oproot: str, - NFRB: int = None, - iFRB: int = 0, - outdir: str = "Output", -): - - # Generate the folder? - if not os.path.isdir(outdir): - os.mkdir(outdir) - - ############## Load up ############## - input_dict = io.process_jfile(pfile) - - # Deconstruct the input_dict - state_dict, cube_dict, vparam_dict = it.parse_input_dict(input_dict) - - npoints = np.array([item["n"] for key, item in vparam_dict.items()]) - ntotal = int(np.prod(np.abs(npoints))) - - # Total number of CPUs to be running on this Cube - total_ncpu = pargs.ncpu if pargs.total_ncpu is None else pargs.total_ncpu - batch = 1 if pargs.batch is None else pargs.batch - - nper_cpu = ntotal // total_ncpu - if int(ntotal / total_ncpu) != nper_cpu: - raise IOError(f"Ncpu={total_ncpu} must divide evenly into ntotal={ntotal}") - - survey_file = os.path.join(resource_filename('zdm', 'craco'), - 'MC_F', 'Surveys', 'F_0.32_survey') - commands = [] - for kk in range(pargs.ncpu): - line = [] - # Which CPU is running out of the total? - iCPU = (batch - 1) * pargs.ncpu + kk - outfile = os.path.join(outdir, oproot.replace(".csv", f"{iCPU+1}.csv")) - # Command - line = [ - "zdm_build_cube", - "-n", - f"{iCPU+1}", - "-m", - f"{nper_cpu}", - "-o", - f"{outfile}", - "-s", - f"{survey_file}", - "--clobber", - "-p", - f"{pfile}", - ] - # NFRB? - if NFRB is not None: - line += [f"--NFRB", f"{NFRB}"] - # iFRB? - if iFRB > 0: - line += [f"--iFRB", f"{iFRB}"] - # Finish - # line += ' & \n' - commands.append(line) - - # Launch em! - processes = [] - - for command in commands: - # Popen - print(f"Running this command: {' '.join(command)}") - pw = subprocess.Popen(command) - processes.append(pw) - - # Wait on em! - for pw in processes: - pw.wait() - - print("All done!") - - -def parse_option(): - # test for command-line arguments here - parser = argparse.ArgumentParser() - parser.add_argument( - "-n", - "--ncpu", - type=int, - required=True, - help="Number of CPUs to run on (might be split in batches)", - ) - parser.add_argument( - "-t", - "--total_ncpu", - type=int, - required=False, - help="Total number of CPUs to run on (might be split in batches)", - ) - parser.add_argument( - "-b", "--batch", type=int, default=1, required=False, help="Batch number" - ) - # parser.add_argument('--NFRB',type=int,required=False,help="Number of FRBs to analzye") - # parser.add_argument('--iFRB',type=int,default=0,help="Initial FRB to run from") - args = parser.parse_args() - - return args - - -if __name__ == "__main__": - # get the argument of training. - pfile = "../Cubes/craco_mini_cube.json" - oproot = "craco_mini.csv" - pargs = parse_option() - main(pargs, pfile, oproot, NFRB=100, iFRB=100) diff --git a/papers/F/Analysis/CRACO/Cloud/run_craco_mini_logF.py b/papers/F/Analysis/CRACO/Cloud/run_craco_mini_logF.py index 0322aa8d..64b1174b 100644 --- a/papers/F/Analysis/CRACO/Cloud/run_craco_mini_logF.py +++ b/papers/F/Analysis/CRACO/Cloud/run_craco_mini_logF.py @@ -1,4 +1,6 @@ -""" Run a Nautilus test """ +""" +This script generates the `.csv` files for a miniature synthetic likelihood cube using the CRACO data. +""" # It should be possible to remove all the matplotlib calls from this # but in the current implementation it is not removed. diff --git a/papers/F/Analysis/CRACO/Contour/lower_CI.py b/papers/F/Analysis/CRACO/Contour/lower_CI.py deleted file mode 100644 index 7158df20..00000000 --- a/papers/F/Analysis/CRACO/Contour/lower_CI.py +++ /dev/null @@ -1,258 +0,0 @@ -import numpy as np -import zdm -import matplotlib.pyplot as plt -from frb.dm import cosmic -from zdm.pcosmic import pcosmic, get_mean_DM -from zdm.parameters import State -import scipy.stats - -from IPython import embed - -fC0 = cosmic.grab_C0_spline() - - -def lower_ci(data, conflevel=0.95): - mu, sigma = np.mean(data), scipy.stats.sem(data) - k = sigma * scipy.stats.t.ppf((1 + conflevel) / 2.0, len(data) - 1) - return mu - k - - -def lowerCI_F( - Fs, - H0=None, - z=0.5, - deltas=np.linspace(0.01, 5, 200), - niter_per_F=1000, - ns_per_F=1000, -): - - z = np.array(z).reshape(1) - - n = len(Fs) - - lower_cis = np.zeros(n) - - state = State() - - if H0 is not None: - state.update_params({"H0": H0}) - else: - H0 = state.cosmo.H0 - - mean_dm_cosmic = get_mean_DM(z, state) - - for k, F in enumerate(Fs): - - sigma = F / np.sqrt(z) - C0 = fC0(sigma) - pdelta = zdm.pcosmic.pcosmic(deltas, z, F, C0) - - # Perform a bootstrap CI for `niter_per_F` iterations - lower_cis_at_F = np.zeros(niter_per_F) - for i in range(niter_per_F): - sample = np.random.choice( - deltas, p=pdelta / np.sum(pdelta), size=ns_per_F, replace=True - ) - lower_cis_at_F[i] = lower_ci(sample) * mean_dm_cosmic - - # Get the mean lower 95 pct CI from the bootstrap - lower_cis[k] = np.mean(lower_cis_at_F) - - return {"F": Fs, "lower.ci": lower_cis, "H0": np.ones(n) * H0} - - -def lowerCI_H0( - H0s, - F=None, - z=0.5, - deltas=np.linspace(0.01, 5, 200), - niter_per_H0=1000, - ns_per_H0=1000, -): - - z = np.array(z).reshape(1) - - n = len(H0s) - - lower_cis = np.zeros(n) - - state = State() - - if F is not None: - state.update_params({"F": F}) - else: - F = state.IGM.F - - sigma = F / np.sqrt(z) - C0 = fC0(sigma) - pdelta = zdm.pcosmic.pcosmic(deltas, z, F, C0) - - for k, H0 in enumerate(H0s): - - state.update_params({"H0": H0}) - - mean_dm_cosmic = get_mean_DM(z, state) - - # Perform a bootstrap CI for `niter_per_F` iterations - lower_cis_at_H0 = np.zeros(niter_per_H0) - for i in range(niter_per_H0): - - sample = np.random.choice( - deltas, p=pdelta / np.sum(pdelta), size=ns_per_H0, replace=True - ) - - lower_cis_at_H0[i] = lower_ci(sample) * mean_dm_cosmic - - # Get the mean lower 95 pct CI from the bootstrap - lower_cis[k] = np.mean(lower_cis_at_H0) - - return {"H0": H0s, "lower.ci": lower_cis, "F": np.ones(n) * F} - - -def make_plots_F( - Fs, - H0=None, - z=0.5, - deltas=np.linspace(0.01, 5, 200), - niter_per_F=1000, - ns_per_F=1000, - outfile="F_plot.png", -): - - df = lowerCI_F( - Fs, - H0=H0, - z=0.5, - deltas=np.linspace(0.01, 5, 200), - niter_per_F=1000, - ns_per_F=1000, - ) - - H0 = df["H0"][0] - - fig, ax = plt.subplots(dpi=200) - ax.scatter(df["F"], df["lower.ci"]) - ax.set_title(f"H0 = {H0}, z = {z}") - ax.set_xlabel(f"$F$") - ax.set_ylabel(f"Lower CI of $p(\Delta)$") - plt.savefig(outfile) - - -def make_plots_H0( - H0s, - F=None, - z=0.5, - deltas=np.linspace(0.01, 5, 200), - niter_per_H0=1000, - ns_per_H0=1000, - outfile="H0_plot.png", -): - - df = lowerCI_H0( - H0s, - F=F, - z=0.5, - deltas=np.linspace(0.01, 5, 200), - niter_per_H0=1000, - ns_per_H0=1000, - ) - - F = df["F"][0] - - fig, ax = plt.subplots(dpi=200) - ax.scatter(df["H0"], df["lower.ci"]) - ax.set_title(f"F = {F}, z = {z}") - ax.set_xlabel(f"$H_0$") - ax.set_ylabel(f"Lower CI of $p(\Delta)$") - plt.savefig(outfile) - - -def lower_CI_grid( - H0s, - Fs, - deltas=np.linspace(0.01, 5, 200), - z=0.5, - niter_per_param=1000, - ns_per_param=1000, - make_plot=False, -): - - state = State() - - lower_cis = np.zeros((len(H0s), len(Fs))) - - for i, H0 in enumerate(H0s): - for j, F in enumerate(Fs): - state.update_params({"H0": H0, "F": F}) - - z = np.array(z).reshape(1) - mean_dm_cosmic = get_mean_DM(z, state) - - sigma = F / np.sqrt(z) - C0 = fC0(sigma) - pdelta = zdm.pcosmic.pcosmic(deltas, z, F, C0) - - lower_cis_at_pt = np.zeros(niter_per_param) - - for u in range(niter_per_param): - sample = np.random.choice( - deltas, p=pdelta / np.sum(pdelta), size=ns_per_param, replace=True - ) - - lower_cis_at_pt[u] = lower_ci(sample) * mean_dm_cosmic - - lower_cis[i, j] = np.mean(lower_cis_at_pt) - - if make_plot: - outfile = f"lower_CI_grid_z_{z[0]}.png" - fig, ax = plt.subplots(dpi=200) - - x, y = np.meshgrid(H0s, Fs) - - c = ax.pcolormesh(x, y, lower_cis.T, cmap="jet", shading="auto") - plt.colorbar(c, label="DM (Lower CI)") - - ax.set_title(f"z = {z[0]}") - ax.set_xlabel(f"$H_0$") - ax.set_ylabel(f"$F$") - - plt.savefig(outfile, bbox_inches="tight") - - return lower_cis - - -# make_plots_F(np.linspace(0.1, 1, 20), z=0.5, outfile="F_plot_z_0.5.png") -# make_plots_H0(np.linspace(50, 80, 20), z=0.5, outfile="H0_plot_z_0.5.png") - -# make_plots_F(np.linspace(0.1, 1, 20), z=0.25, outfile="F_plot_z_0.25.png") -# make_plots_H0(np.linspace(50, 80, 20), z=0.25, outfile="H0_plot_z_0.25.png") - -# make_plots_F(np.linspace(0.1, 1, 20), z=0.1, outfile="F_plot_z_0.1.png") -# make_plots_H0(np.linspace(50, 80, 20), z=0.1, outfile="H0_plot_z_0.1.png") - -# make_plots_F(np.linspace(0.1, 1, 20), z=1.5, outfile="F_plot_z_1.5.png") -# make_plots_H0(np.linspace(50, 80, 20), z=1.5, outfile="H0_plot_z_1.5.png") - -# make_plots_F(np.linspace(0.1, 1, 20), H0=55, z=0.25, outfile="F_plot_z_0.25_alt.png") -# make_plots_H0(np.linspace(50, 80, 20), F=0.8, z=0.25, outfile="H0_plot_z_0.25_alt.png") - -lower_CI_grid( - H0s=np.linspace(55, 80, num=20), - Fs=np.linspace(0.01, 1, num=20), - z=0.5, - make_plot=True, -) - -lower_CI_grid( - H0s=np.linspace(55, 80, num=20), - Fs=np.linspace(0.01, 1, num=20), - z=0.15, - make_plot=True, -) - -lower_CI_grid( - H0s=np.linspace(55, 80, num=20), - Fs=np.linspace(0.01, 1, num=20), - z=0.05, - make_plot=True, -) diff --git a/papers/F/Analysis/CRACO/Contour/pdelta.py b/papers/F/Analysis/CRACO/Contour/pdelta.py index 01c8abe5..47b387e8 100644 --- a/papers/F/Analysis/CRACO/Contour/pdelta.py +++ b/papers/F/Analysis/CRACO/Contour/pdelta.py @@ -1,3 +1,6 @@ +""" +Plots p(Delta = DM_cosmic / ) at a fixed redshift for different F values. +""" import numpy as np import zdm import matplotlib.pyplot as plt @@ -6,13 +9,24 @@ from zdm.parameters import State import scipy.stats +# Grabs the C_0 spline from the cosmic module to ensure p(Delta) is centered at 1 fC0 = cosmic.grab_C0_spline() - def makePDeltaPlot_F(deltas, F, z, outfile=None): + """ + Plots p(Delta = DM_cosmic / ) at a fixed redshift for a single F value. + """ + + # Calculate sigma_DM sigma = F / np.sqrt(z) + + # Grab C_0 spline C0 = fC0(sigma) + + # Calculate p(Delta) pdelta = zdm.pcosmic.pcosmic(deltas, z, F, C0) + + # Plot fig, ax = plt.subplots(dpi=200) ax.plot(deltas, pdelta, c="k") if outfile is None: @@ -23,8 +37,10 @@ def makePDeltaPlot_F(deltas, F, z, outfile=None): plt.savefig(outfile) -def test(deltas, Fs, z, colors, outfile=None): - +def makePDeltaPlot_varyF(deltas, Fs, z, colors, outfile=None): + """ + Plots p(Delta = DM_cosmic / ) at a fixed redshift for different F values. + """ fig, ax = plt.subplots(figsize=(5, 4), dpi=200) for i, F in enumerate(Fs): @@ -41,37 +57,11 @@ def test(deltas, Fs, z, colors, outfile=None): ax.legend() plt.savefig(outfile, bbox_inches="tight") +makePDeltaPlot_F(np.linspace(0.01, 2.5, 100), 0.32, 1) +makePDeltaPlot_F(np.linspace(0.01, 2.5, 100), 0.01, 1) +makePDeltaPlot_F(np.linspace(0.01, 2.5, 100), 1, 1) -def test2(deltas, Fs, z, colors, outfile=None): - - state = State() - - fig, ax = plt.subplots(figsize=(5, 4), dpi=200) - - for i, F in enumerate(Fs): - - state.update_params({"F": F}) - - sigma = F / np.sqrt(z) - C0 = fC0(sigma) - pdelta = zdm.pcosmic.pcosmic(deltas, z, F, C0) - mean_DM = get_mean_DM(np.array(z).reshape(1), state) - ax.plot(deltas * mean_DM, pdelta, c=colors[i], label=f"F = {F}") - - if outfile is None: - outfile = f"pdelta_test_2.png" - ax.set_xlabel(r"$\rm{DM_{EG}}$") - ax.set_ylabel(r"$p(\rm{DM_{EG}})$") - ax.set_title(f"z={z}") - ax.legend() - plt.savefig(outfile, bbox_inches="tight") - - -# makePDeltaPlot_F(np.linspace(0.01, 2.5, 100), 0.32, 1) -# makePDeltaPlot_F(np.linspace(0.01, 2.5, 100), 0.01, 1) -# makePDeltaPlot_F(np.linspace(0.01, 2.5, 100), 1, 1) - -test2( +makePDeltaPlot_varyF( np.linspace(0.01, 2.5, 300), [0.01, 0.9], z=0.5, colors=["r", "orange"], ) diff --git a/papers/F/Analysis/CRACO/make_ll_2D_F.py b/papers/F/Analysis/CRACO/make_ll_2D_F.py index 7f087c97..fbb9f290 100644 --- a/papers/F/Analysis/CRACO/make_ll_2D_F.py +++ b/papers/F/Analysis/CRACO/make_ll_2D_F.py @@ -1,3 +1,6 @@ +""" +This script creates 2D likelihood plots given a `.npz` cube. +""" import numpy as np import os import zdm @@ -13,6 +16,7 @@ def main(verbose=False): if not os.path.exists(opdir): os.mkdir(opdir) + # loads the cube CubeFile='Cubes/craco_full_cube.npz' if os.path.exists(CubeFile): data=np.load(CubeFile) diff --git a/papers/F/Analysis/CRACO/make_ll_2D_H0.py b/papers/F/Analysis/CRACO/make_ll_2D_H0.py deleted file mode 100644 index 11267df8..00000000 --- a/papers/F/Analysis/CRACO/make_ll_2D_H0.py +++ /dev/null @@ -1,138 +0,0 @@ -import numpy as np -import os -import zdm -from zdm import analyze_cube as ac - -from matplotlib import pyplot as plt -from IPython import embed - -def main(verbose=False): - - # output directory - opdir="figs/" - if not os.path.exists(opdir): - os.mkdir(opdir) - - CubeFile='Cubes/craco_mini_cube.npz' - if os.path.exists(CubeFile): - data=np.load(CubeFile) - else: - print("Could not file cube output file ",CubeFile) - print("Please obtain it from [repository]") - exit() - - if verbose: - print("Data file contains the following items") - for thing in data: - print(thing) - - lst = data.files - lldata=data["ll"] - params=data["params"] - - def get_param_values(data,params): - """ - Gets the unique values of the data from a cube output - Currently the parameter order is hard-coded - - """ - param_vals=[] - for param in params: - col=data[param] - unique=np.unique(col) - param_vals.append(unique) - return param_vals - - param_vals=get_param_values(data, params) - - # builds uvals list - uvals=[] - latexnames=[] - for ip,param in enumerate(data["params"]): - # switches for alpha - if param=="alpha": - uvals.append(data[param]*-1.) - else: - uvals.append(data[param]) - if param=="alpha": - latexnames.append('$\\alpha$') - ialpha=ip - elif param=="lEmax": - latexnames.append('$\\log_{10} E_{\\rm max}$') - elif param=="H0": - latexnames.append('$H_0$') - elif param=="gamma": - latexnames.append('$\\gamma$') - elif param=="sfr_n": - latexnames.append('$n_{\\rm sfr}$') - elif param=="lmean": - latexnames.append('$\\mu_{\\rm host}$') - elif param=="lsigma": - latexnames.append('$\\sigma_{\\rm host}$') - elif param=="logF": - latexnames.append('$\\log_{10} F$') - - #latexnames=['$\\log_{10} E_{\\rm max}$','$H_0$','$\\alpha$','$\\gamma$','$n_{\\rm sfr}$','$\\mu_{\\rm host}$','$\\sigma_{\\rm host}$'] - - list2=[] - vals2=[] - # gets Bayesian posteriors - deprecated,uw_vectors,wvectors=ac.get_bayesian_data(data["ll"]) - for i,vec in enumerate(uw_vectors): - n=np.argmax(vec) - val=uvals[i][n] - if params[i] != "H0": - list2.append(params[i]) - vals2.append(val) - else: - iH0=i - - ###### NOTATION ##### - # uw: unweighted - # wH0: weighted according to H0 knowledged - # f: fixed other parameters - # B: best-fit - - ############## 2D plots at best-fit valuess ########## - - # gets the slice corresponding to the best-fit values of all other parameters - # this is 1D, so is our limit on H0 keeping all others fixed - for i,item in enumerate(list2): - - list3=np.concatenate((list2[0:i],list2[i+1:])) - vals3=np.concatenate((vals2[0:i],vals2[i+1:])) - array=ac.get_slice_from_parameters(data,list3,vals3) - - # log to lin space - array[np.isnan(array)] = -1e99 - array -= np.max(array) - array = 10**array - array /= np.sum(array) - - # now have array for slice covering best-fit values - if i < iH0: - modi=i - else: - modi=i+1 - #array=array.T - array=array.swapaxes(0,1) - savename=opdir+"/lls_"+params[iH0]+"_"+params[modi]+".png" - -# if (latexnames[modi] == '$\\gamma$'): -# embed(header="gamma") - -# if (latexnames[modi] == '$H_0$'): -# embed(header="H0") - - if params[modi]=="alpha": - #switches order of array in alpha dimension - array=np.flip(array,axis=0) - ac.make_2d_plot(array,latexnames[modi],latexnames[iH0], - -param_vals[modi],param_vals[iH0], - savename=savename,norm=1) - else: - ac.make_2d_plot(array,latexnames[modi],latexnames[iH0], - param_vals[modi],param_vals[iH0], - savename=savename,norm=1) - -main() \ No newline at end of file diff --git a/papers/F/Analysis/CRACO/py/craco_qck_explore.py b/papers/F/Analysis/CRACO/py/craco_qck_explore.py index 6d78228c..2beab5b7 100644 --- a/papers/F/Analysis/CRACO/py/craco_qck_explore.py +++ b/papers/F/Analysis/CRACO/py/craco_qck_explore.py @@ -1,3 +1,8 @@ +""" +Generates 1D likelihood PDFs of each parameter from a `.npz` cube. + +The only argument in running the file corresponds to a hard-coded location of the `.npz` cube file. +""" # imports from importlib import reload import numpy as np @@ -16,17 +21,18 @@ def main(pargs): jroot = None - if pargs.run == "mini": - scube = "mini" - outdir = "Mini/" - elif pargs.run == "F": + + if pargs.run == "F": + # 2D cube run with H0 and F scube = "H0_F" outdir = "H0_F/" elif pargs.run == "H0_logF": + # 2D cube run with H0 and logF scube = "H0_logF" outdir = "H0_logF/" # Main # elif pargs.run == "logF_full": + # Full CRACO likelihood cube scube = "full" outdir = "logF_Full/" @@ -135,4 +141,4 @@ def parse_option(): pargs = parse_option() main(pargs) -# python py/craco_qck_explore.py mini +# python py/craco_qck_explore.py logF_full \ No newline at end of file diff --git a/papers/F/Analysis/CRACO/py/cube_test.ipynb b/papers/F/Analysis/CRACO/py/cube_test.ipynb deleted file mode 100644 index 20a59871..00000000 --- a/papers/F/Analysis/CRACO/py/cube_test.ipynb +++ /dev/null @@ -1,134 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "cube = np.load(\"../Cubes/craco_H0_logF_cube.npz\")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(50, 50)" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ll = cube[\"ll\"]\n", - "ll.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "logF = cube[\"logF\"]\n", - "H0 = cube[\"H0\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "dF = logF[1]-logF[0]\n", - "dH = H0[1] - H0[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "ll[np.isnan(ll)]=-1e99\n", - "ll -= ll.max()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots()\n", - "\n", - "im=ax.imshow(ll.T,cmap='jet',origin='lower', \n", - " interpolation='None', extent=[logF.min()-dF/2, logF.max()+dF/2, 55.-dH/2, 80+dH/2], aspect='auto', vmin=-4., vmax=0.)\n", - "# Color bar\n", - "cbar=plt.colorbar(im,fraction=0.046, shrink=1.2,aspect=15,pad=0.05)\n", - "cbar.set_label(r'$\\Delta$ Log10 Likelihood')\n", - "\n", - "ax.set_xlabel(f'$\\log F$')\n", - "ax.set_ylabel('H0 (km/s/Mpc)')\n", - "plt.savefig('fig_H0_vs_F.png', dpi=200)\n", - "plt.show()\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.8.5 ('base')", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "9731a3b7ebb41545a410367c249afbc4842fd5740da82f1bd29e6ac1d7253e6e" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/papers/F/Analysis/CRACO/py/slurp_craco_cubes.py b/papers/F/Analysis/CRACO/py/slurp_craco_cubes.py index a4023838..0e9a6a66 100644 --- a/papers/F/Analysis/CRACO/py/slurp_craco_cubes.py +++ b/papers/F/Analysis/CRACO/py/slurp_craco_cubes.py @@ -1,34 +1,16 @@ -""" Simple script to slurp """ +""" +Script to intake the `.csv` files from the CRACO runs and convert them to a single `.npz` file. + +The only argument in running the file corresponds to a hard-coded location of the `.csv` files and the cube `.json` file. +""" from zdm import analyze_cube def main(pargs): - if pargs.run == "Emax": - # Emax - input_file = "Cubes/craco_H0_Emax_cube.json" - prefix = "Cubes/tmp" - nsurveys = 1 - - # Run it - analyze_cube.slurp_cube( - input_file, prefix, "Cubes/craco_H0_Emax_cube.npz", nsurveys - ) - - elif pargs.run == "F": - # Emax - input_file = "Cubes/craco_H0_F_cube.json" - prefix = "Cloud/Output/craco_H0_F" - nsurveys = 1 - - # Run it - analyze_cube.slurp_cube( - input_file, prefix, "Cubes/craco_H0_F_cube.npz", nsurveys - ) - - elif pargs.run == "logF": - # Emax + if pargs.run == "logF": + # 2D cube run with H0 and logF input_file = "Cubes/craco_H0_logF_cube.json" prefix = "Cloud/Output_logF_test/craco_H0_logF" nsurveys = 1 @@ -39,7 +21,7 @@ def main(pargs): ) elif pargs.run == "logF_full": - # Emax + # Full CRACO likelihood cube input_file = "Cubes/craco_full_cube.json" prefix = "Cloud/OutputFull/craco_full" nsurveys = 1 @@ -49,92 +31,6 @@ def main(pargs): input_file, prefix, "Cubes/craco_full_cube.npz", nsurveys ) - elif pargs.run == "lmF": - # Emax - input_file = "Cubes/craco_lm_F_cube.json" - prefix = "Cloud/Output/craco_lm_F" - nsurveys = 1 - - # Run it - analyze_cube.slurp_cube( - input_file, prefix, "Cubes/craco_lm_F_cube.npz", nsurveys - ) - - elif pargs.run == "mini": - # Emax - input_file = "Cubes/craco_mini_cube.json" - # prefix = 'Cubes/craco_mini' - prefix = "Cloud/OutputMini/craco_mini" - nsurveys = 1 - - # Run it - analyze_cube.slurp_cube( - input_file, prefix, "Cubes/craco_mini_cube.npz", nsurveys - ) - elif pargs.run == "submini": - # Emax - input_file = "Cubes/craco_submini_cube.json" - prefix = "Cubes/craco_submini_cube" - nsurveys = 1 - - # Run it - analyze_cube.slurp_cube( - input_file, prefix, "Cubes/craco_submini_cube.npz", nsurveys - ) - - elif pargs.run == "sfrEmax": - # Emax - input_file = "Cubes/craco_sfr_Emax_cube.json" - prefix = "Cubes/craco_sfr_Emax_cube" - nsurveys = 1 - - # Run it - analyze_cube.slurp_cube( - input_file, prefix, "Cubes/craco_sfr_Emax_cube.npz", nsurveys - ) - - elif pargs.run == "alphaEmax": - # Emax - input_file = "Cubes/craco_alpha_Emax_cube.json" - prefix = "Cubes/craco_alpha_Emax_cube" - nsurveys = 1 - - # Run it - analyze_cube.slurp_cube( - input_file, prefix, "Cubes/craco_alpha_Emax_cube.npz", nsurveys - ) - elif pargs.run == "full": - # Emax - input_file = "Cubes/craco_full_cube.json" - prefix = "Cubes/craco_full" - nsurveys = 1 - - # Run it - analyze_cube.slurp_cube( - input_file, prefix, "Cubes/craco_full_cube.npz", nsurveys - ) - elif pargs.run == "another_full": - # Emax - input_file = "Cubes/craco_full_cube.json" - prefix = "Cubes/craco_400_full" - nsurveys = 1 - - # Run it - analyze_cube.slurp_cube( - input_file, prefix, "Cubes/craco_400_full_cube.npz", nsurveys - ) - elif pargs.run == "third_full": - # Emax - input_file = "Cubes/craco_full_cube.json" - prefix = "Cubes/craco_3rd_full" - nsurveys = 1 - - # Run it - analyze_cube.slurp_cube( - input_file, prefix, "Cubes/craco_3rd_full_cube.npz", nsurveys - ) - - def parse_option(): """ This is a function used to parse the arguments in the training. @@ -159,7 +55,4 @@ def parse_option(): pargs = parse_option() main(pargs) -# python py/slurp_craco_cubes.py mini -# python py/slurp_craco_cubes.py another_full - -# python py/slurp_craco_cubes.py F +# python py/slurp_craco_cubes.py logF_full diff --git a/papers/F/Analysis/Real/Cloud/run_craco_real.py b/papers/F/Analysis/Real/Cloud/run_craco_real.py index 726ce870..9a80aa23 100644 --- a/papers/F/Analysis/Real/Cloud/run_craco_real.py +++ b/papers/F/Analysis/Real/Cloud/run_craco_real.py @@ -1,5 +1,6 @@ -""" Run a Nautilus test """ - +""" +This script generates the `.csv` files for the likelihood cube using real FRB observations (see Baptista+23) +""" # It should be possible to remove all the matplotlib calls from this # but in the current implementation it is not removed. import argparse diff --git a/papers/F/Analysis/Real/Cloud/run_real_craco_block.py b/papers/F/Analysis/Real/Cloud/run_real_craco_block.py index 4705dd34..4537f940 100644 --- a/papers/F/Analysis/Real/Cloud/run_real_craco_block.py +++ b/papers/F/Analysis/Real/Cloud/run_real_craco_block.py @@ -1,3 +1,8 @@ +""" +This script generates the `.csv` files for the likelihood cube using real FRB observations (see Baptista+23) +This script is modified to generate specific cube `.csv` files between a range of numbers that correspond to the indices along the H_0 dimension in the cube. +""" + # Running this command: python ../py/build_real_cube.py -n 1 -m 3000 -o Output/craco_real1.csv --clobber -p ../Cubes/craco_real_cube.json import argparse diff --git a/papers/F/Analysis/Real/Cloud/run_real_mini.py b/papers/F/Analysis/Real/Cloud/run_real_mini.py index aa137160..24497f7a 100644 --- a/papers/F/Analysis/Real/Cloud/run_real_mini.py +++ b/papers/F/Analysis/Real/Cloud/run_real_mini.py @@ -1,5 +1,6 @@ -""" Run a Nautilus test """ - +""" +This script generates the `.csv` files for a smaller likelihood cube using real FRB observations (see Baptista+23) +""" # It should be possible to remove all the matplotlib calls from this # but in the current implementation it is not removed. import argparse diff --git a/papers/F/Analysis/Real/make_ll_2D_F.py b/papers/F/Analysis/Real/make_ll_2D_F.py index 3c2939de..dfd12a19 100644 --- a/papers/F/Analysis/Real/make_ll_2D_F.py +++ b/papers/F/Analysis/Real/make_ll_2D_F.py @@ -1,3 +1,6 @@ +""" +This script creates 2D likelihood plots given a `.npz` cube. +""" import numpy as np import os import zdm diff --git a/papers/F/Analysis/Real/make_ll_2D_H0.py b/papers/F/Analysis/Real/make_ll_2D_H0.py deleted file mode 100644 index ede07066..00000000 --- a/papers/F/Analysis/Real/make_ll_2D_H0.py +++ /dev/null @@ -1,152 +0,0 @@ -import numpy as np -import os -import zdm -from zdm import analyze_cube as ac - -from matplotlib import pyplot as plt -from IPython import embed - - -def main(verbose=False): - - # output directory - opdir = "figs/" - if not os.path.exists(opdir): - os.mkdir(opdir) - - CubeFile = "Cubes/craco_real_cube.npz" - if os.path.exists(CubeFile): - data = np.load(CubeFile) - else: - print("Could not file cube output file ", CubeFile) - print("Please obtain it from [repository]") - exit() - - if verbose: - print("Data file contains the following items") - for thing in data: - print(thing) - - lst = data.files - lldata = data["ll"] - params = data["params"] - - def get_param_values(data, params): - """ - Gets the unique values of the data from a cube output - Currently the parameter order is hard-coded - - """ - param_vals = [] - for param in params: - col = data[param] - unique = np.unique(col) - param_vals.append(unique) - return param_vals - - param_vals = get_param_values(data, params) - - # builds uvals list - uvals = [] - latexnames = [] - for ip, param in enumerate(data["params"]): - # switches for alpha - if param == "alpha": - uvals.append(data[param] * -1.0) - else: - uvals.append(data[param]) - if param == "alpha": - latexnames.append("$\\alpha$") - ialpha = ip - elif param == "lEmax": - latexnames.append("$\\log_{10} E_{\\rm max}$") - elif param == "H0": - latexnames.append("$H_0$") - elif param == "gamma": - latexnames.append("$\\gamma$") - elif param == "sfr_n": - latexnames.append("$n_{\\rm sfr}$") - elif param == "lmean": - latexnames.append("$\\mu_{\\rm host}$") - elif param == "lsigma": - latexnames.append("$\\sigma_{\\rm host}$") - elif param == "logF": - latexnames.append("$\\log_{10} F$") - - # latexnames=['$\\log_{10} E_{\\rm max}$','$H_0$','$\\alpha$','$\\gamma$','$n_{\\rm sfr}$','$\\mu_{\\rm host}$','$\\sigma_{\\rm host}$'] - - list2 = [] - vals2 = [] - # gets Bayesian posteriors - deprecated, uw_vectors, wvectors = ac.get_bayesian_data(data["ll"]) - for i, vec in enumerate(uw_vectors): - n = np.argmax(vec) - val = uvals[i][n] - if params[i] != "H0": - list2.append(params[i]) - vals2.append(val) - else: - iH0 = i - - ###### NOTATION ##### - # uw: unweighted - # wH0: weighted according to H0 knowledged - # f: fixed other parameters - # B: best-fit - - ############## 2D plots at best-fit valuess ########## - - # gets the slice corresponding to the best-fit values of all other parameters - # this is 1D, so is our limit on H0 keeping all others fixed - for i, item in enumerate(list2): - - list3 = np.concatenate((list2[0:i], list2[i + 1 :])) - vals3 = np.concatenate((vals2[0:i], vals2[i + 1 :])) - array = ac.get_slice_from_parameters(data, list3, vals3) - - # log to lin space - array[np.isnan(array)] = -1e99 - array -= np.max(array) - array = 10 ** array - array /= np.sum(array) - - # now have array for slice covering best-fit values - if i < iH0: - modi = i - else: - modi = i + 1 - # array=array.T - array = array.swapaxes(0, 1) - savename = opdir + "/lls_" + params[iH0] + "_" + params[modi] + ".png" - - # if (latexnames[modi] == '$\\gamma$'): - # embed(header="gamma") - - # if (latexnames[modi] == '$H_0$'): - # embed(header="H0") - - if params[modi] == "alpha": - # switches order of array in alpha dimension - array = np.flip(array, axis=0) - ac.make_2d_plot( - array, - latexnames[modi], - latexnames[iH0], - -param_vals[modi], - param_vals[iH0], - savename=savename, - norm=1, - ) - else: - ac.make_2d_plot( - array, - latexnames[modi], - latexnames[iH0], - param_vals[modi], - param_vals[iH0], - savename=savename, - norm=1, - ) - - -main() diff --git a/papers/F/Analysis/Real/make_survey_contrib_fig.py b/papers/F/Analysis/Real/make_survey_contrib_fig.py index 62c4157d..0807abc5 100644 --- a/papers/F/Analysis/Real/make_survey_contrib_fig.py +++ b/papers/F/Analysis/Real/make_survey_contrib_fig.py @@ -1,12 +1,5 @@ """ -This is a script used to produce figures for fig 7 - -It generates two sets of results: -- constraints on alpha (in directory fig8_alphaSingleFigures) -- constraints on other 5 non-H0 parameters (in directory fig_othersSingleFigures) - -Alpha requires special treatment due to the prior not covering -the full range of possible values. +This is a script used to produce figures for the survey contributions for each parameter in the cube. """ import numpy as np diff --git a/papers/F/Analysis/Real/py/craco_qck_explore.py b/papers/F/Analysis/Real/py/craco_qck_explore.py index 9050c7b5..dc93350a 100644 --- a/papers/F/Analysis/Real/py/craco_qck_explore.py +++ b/papers/F/Analysis/Real/py/craco_qck_explore.py @@ -1,3 +1,9 @@ +""" +Generates 1D likelihood PDFs of each parameter from a `.npz` cube. + +The only argument in running the file corresponds to a hard-coded location of the `.npz` cube file. +""" + # imports from importlib import reload import numpy as np diff --git a/papers/F/Analysis/Real/py/slurp_craco_cubes.py b/papers/F/Analysis/Real/py/slurp_craco_cubes.py index 1bd99902..094836dc 100644 --- a/papers/F/Analysis/Real/py/slurp_craco_cubes.py +++ b/papers/F/Analysis/Real/py/slurp_craco_cubes.py @@ -1,4 +1,8 @@ -""" Simple script to slurp """ +""" +Script to intake the `.csv` files from the CRACO runs and convert them to a single `.npz` file. + +The only argument in running the file corresponds to a hard-coded location of the `.csv` files and the cube `.json` file. +""" from zdm import analyze_cube diff --git a/papers/F/Analysis/py/analy_F_I.py b/papers/F/Analysis/py/analy_F_I.py index f94c129e..e9f2ecd0 100644 --- a/papers/F/Analysis/py/analy_F_I.py +++ b/papers/F/Analysis/py/analy_F_I.py @@ -1,10 +1,14 @@ +""" +Helper function to load the default survey and grid for CRACO with F parameter +""" + from zdm.craco import loading +# Load the default survey and grid with F fiducial_survey = "../MC_F/Surveys/F_0.32_survey" - def craco_mc_survey_grid(iFRB=100): - """ Load the defaul MonteCarlo survey+grid for CRACO """ + """ Load the default MonteCarlo survey+grid for CRACO """ survey, grid = loading.survey_and_grid( survey_name=fiducial_survey, NFRB=100, lum_func=2, iFRB=iFRB ) diff --git a/papers/F/Analysis/py/makeCornerPlot.ipynb b/papers/F/Analysis/py/makeCornerPlot.ipynb new file mode 100644 index 00000000..835f3603 --- /dev/null +++ b/papers/F/Analysis/py/makeCornerPlot.ipynb @@ -0,0 +1,243 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import os\n", + "import zdm\n", + "import scipy\n", + "from zdm import analyze_cube as ac\n", + "from IPython import embed\n", + "import matplotlib.pyplot as plt\n", + "\n", + "real_data = False\n", + "\n", + "cube = \"../CRACO/Cubes/craco_full_cube.npz\"\n", + "if real_data:\n", + " cube = \"../Real/Cubes/craco_real_cube.npz\"\n", + "\n", + "data = np.load(cube)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "ivalues, lls, wlls = ac.get_bayesian_data(data['ll'])\n", + "\n", + "pH0_idx = np.where(data[\"params\"] == \"H0\")[0][0]\n", + "pH0 = lls[pH0_idx]\n", + "H0s = data[\"H0\"]\n", + "\n", + "plmean_idx = np.where(data[\"params\"] == \"lmean\")[0][0]\n", + "plmean = lls[plmean_idx]\n", + "lmeans = data[\"lmean\"]\n", + "\n", + "plsigma_idx = np.where(data[\"params\"] == \"lsigma\")[0][0]\n", + "plsigma = lls[plsigma_idx]\n", + "lsigmas = data[\"lsigma\"]\n", + "\n", + "plogF_idx = np.where(data[\"params\"] == \"logF\")[0][0]\n", + "plogF = lls[plogF_idx]\n", + "logFs = data[\"logF\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jaybaptista/Desktop/UCSC/code.tmp/zdm/zdm/analyze_cube.py:570: RuntimeWarning: All-NaN slice encountered\n", + " wthemax = np.nanmax(wlls)\n" + ] + } + ], + "source": [ + "uvals, ijs, arrays, warrays = ac.get_2D_bayesian_data(data['ll'])\n", + "\n", + "p = (1-0.68)/2\n", + "\n", + "def getInterpolatedLimits(x, y, p=(1-0.68)/2, nbins=400):\n", + " f = scipy.interpolate.interp1d(x, y, kind='cubic')\n", + " xs = np.linspace(np.min(x), np.max(x), nbins)\n", + " ys = f(xs)\n", + " x_lower, x_upper, _, _ = ac.extract_limits(xs, ys, p)\n", + " return x_lower, x_upper\n", + "\n", + "H0_lower, H0_upper = getInterpolatedLimits(H0s, lls[pH0_idx], p)\n", + "lmean_lower, lmean_upper = getInterpolatedLimits(lmeans, lls[plmean_idx], p)\n", + "lsigma_lower, lsigma_upper = getInterpolatedLimits(lsigmas, lls[plsigma_idx], p)\n", + "F_lower, F_upper = getInterpolatedLimits(logFs, lls[plogF_idx], p)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=(10,10), dpi=300)\n", + "\n", + "nparams = len(data[\"params\"])\n", + "params = data[\"params\"]\n", + "\n", + "params_tex = [\n", + " r\"$H_0$\",\n", + " r\"$\\mu_{\\rm host}$\",\n", + " r\"$\\sigma_{\\rm host}$\",\n", + " r\"$\\log_{10} F$\",\n", + "]\n", + "\n", + "# Diagonals\n", + "for k, param in enumerate(params):\n", + " param_idx = np.where(data[\"params\"] == param)[0][0]\n", + " param_ax = fig.add_subplot(4, 4, (5*k)+1)\n", + " dparam = data[param][1] - data[param][0]\n", + " param_ax.bar(data[param], lls[param_idx], width=dparam, facecolor=\"None\", edgecolor=\"black\")\n", + " param_ax.set_box_aspect(1)\n", + "\n", + " if k > 0:\n", + " param_ax.set_yticklabels([])\n", + "\n", + " if k < nparams-1:\n", + " param_ax.set_xticklabels([])\n", + "\n", + " if k == nparams-1:\n", + " param_ax.set_xlabel(params_tex[-1])\n", + "\n", + "# Off-diagonals\n", + "# H0 and lmean\n", + "ax_H0_lmean = fig.add_subplot(4, 4, 5)\n", + "ax_H0_lmean.imshow(arrays[0].T, origin=\"lower\", aspect=\"auto\", extent=[data[\"H0\"][0], data[\"H0\"][-1], data[\"lmean\"][0], data[\"lmean\"][-1]])\n", + "ax_H0_lmean.set_box_aspect(1)\n", + "ax_H0_lmean.set_ylabel(params_tex[1])\n", + "ax_H0_lmean.set_xticklabels([])\n", + "ax_H0_lmean.axvline(H0_lower, color=\"red\", linestyle=\"--\")\n", + "ax_H0_lmean.axvline(H0_upper, color=\"red\", linestyle=\"--\")\n", + "ax_H0_lmean.axhline(lmean_lower, color=\"red\", linestyle=\"--\")\n", + "ax_H0_lmean.axhline(lmean_upper, color=\"red\", linestyle=\"--\")\n", + "\n", + "# H0 and lsigma\n", + "ax_H0_lsigma = fig.add_subplot(4, 4, 9)\n", + "ax_H0_lsigma.imshow(arrays[1].T, origin=\"lower\", aspect=\"auto\", extent=[data[\"H0\"][0], data[\"H0\"][-1], data[\"lsigma\"][0], data[\"lsigma\"][-1]])\n", + "ax_H0_lsigma.set_box_aspect(1)\n", + "ax_H0_lsigma.set_ylabel(params_tex[2])\n", + "ax_H0_lsigma.set_xticklabels([])\n", + "ax_H0_lsigma.axvline(H0_lower, color=\"red\", linestyle=\"--\")\n", + "ax_H0_lsigma.axvline(H0_upper, color=\"red\", linestyle=\"--\")\n", + "ax_H0_lsigma.axhline(lsigma_lower, color=\"red\", linestyle=\"--\")\n", + "ax_H0_lsigma.axhline(lsigma_upper, color=\"red\", linestyle=\"--\")\n", + "\n", + "# H0 and logF\n", + "ax_H0_logF = fig.add_subplot(4, 4, 13)\n", + "ax_H0_logF.imshow(arrays[2].T, origin=\"lower\", aspect=\"auto\", extent=[data[\"H0\"][0], data[\"H0\"][-1], data[\"logF\"][0], data[\"logF\"][-1]])\n", + "ax_H0_logF.set_box_aspect(1)\n", + "ax_H0_logF.set_xlabel(params_tex[0])\n", + "ax_H0_logF.set_ylabel(params_tex[3])\n", + "ax_H0_logF.axvline(H0_lower, color=\"red\", linestyle=\"--\")\n", + "ax_H0_logF.axvline(H0_upper, color=\"red\", linestyle=\"--\")\n", + "ax_H0_logF.axhline(F_lower, color=\"red\", linestyle=\"--\")\n", + "ax_H0_logF.axhline(F_upper, color=\"red\", linestyle=\"--\")\n", + "\n", + "# lmean and lsigma\n", + "ax_lmean_lsigma = fig.add_subplot(4, 4, 10)\n", + "ax_lmean_lsigma.imshow(arrays[3].T, origin=\"lower\", aspect=\"auto\", extent=[data[\"lmean\"][0], data[\"lmean\"][-1], data[\"lsigma\"][0], data[\"lsigma\"][-1]])\n", + "ax_lmean_lsigma.set_box_aspect(1)\n", + "ax_lmean_lsigma.set_xticklabels([])\n", + "ax_lmean_lsigma.set_yticklabels([])\n", + "ax_lmean_lsigma.axvline(lmean_lower, color=\"red\", linestyle=\"--\")\n", + "ax_lmean_lsigma.axvline(lmean_upper, color=\"red\", linestyle=\"--\")\n", + "ax_lmean_lsigma.axhline(lsigma_lower, color=\"red\", linestyle=\"--\")\n", + "ax_lmean_lsigma.axhline(lsigma_upper, color=\"red\", linestyle=\"--\")\n", + "\n", + "# lmean and logF\n", + "ax_lmean_logF = fig.add_subplot(4, 4, 14)\n", + "ax_lmean_logF.imshow(arrays[4].T, origin=\"lower\", aspect=\"auto\", extent=[data[\"lmean\"][0], data[\"lmean\"][-1], data[\"logF\"][0], data[\"logF\"][-1]])\n", + "ax_lmean_logF.set_box_aspect(1)\n", + "ax_lmean_logF.set_xlabel(params_tex[1])\n", + "ax_lmean_logF.set_yticklabels([])\n", + "ax_lmean_logF.axvline(lmean_lower, color=\"red\", linestyle=\"--\")\n", + "ax_lmean_logF.axvline(lmean_upper, color=\"red\", linestyle=\"--\")\n", + "ax_lmean_logF.axhline(F_lower, color=\"red\", linestyle=\"--\")\n", + "ax_lmean_logF.axhline(F_upper, color=\"red\", linestyle=\"--\")\n", + "\n", + "# lsigma and logF\n", + "ax_lsigma_logF = fig.add_subplot(4, 4, 15)\n", + "ax_lsigma_logF.imshow(arrays[5].T, origin=\"lower\", aspect=\"auto\", extent=[data[\"lsigma\"][0], data[\"lsigma\"][-1], data[\"logF\"][0], data[\"logF\"][-1]])\n", + "ax_lsigma_logF.set_box_aspect(1)\n", + "ax_lsigma_logF.set_xlabel(params_tex[2])\n", + "ax_lsigma_logF.set_yticklabels([])\n", + "ax_lsigma_logF.axvline(lsigma_lower, color=\"red\", linestyle=\"--\")\n", + "ax_lsigma_logF.axvline(lsigma_upper, color=\"red\", linestyle=\"--\")\n", + "ax_lsigma_logF.axhline(F_lower, color=\"red\", linestyle=\"--\")\n", + "ax_lsigma_logF.axhline(F_upper, color=\"red\", linestyle=\"--\")\n", + "\n", + "plt.subplots_adjust(hspace = 0.1, wspace = 0.02)\n", + "if real_data:\n", + " fig.text(0.5, 0.9, \"Observed Constraints\", ha=\"center\", size=16)\n", + "else:\n", + " fig.text(0.5, 0.9, \"Synthetic Constraints\", ha=\"center\", size=16)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/papers/F/Analysis/py/plotF_wH0Prior.py b/papers/F/Analysis/py/plotF_wH0Prior.py index f4de09bb..d99bc664 100644 --- a/papers/F/Analysis/py/plotF_wH0Prior.py +++ b/papers/F/Analysis/py/plotF_wH0Prior.py @@ -1,16 +1,15 @@ """ -This is a script to produce limit plots for a cube +This is a script to produce limit plots for a cube. -It produces three sets of plots: -- single parameter limits with a prior on H0 between Planck and SN1a values -- single parameter limits also showing results with priors on H0 equal to: +The plots produced with priors on H0 are stored in folders prefixed with "wH0". +Plots generated with the synthetic CRACO cube are suffixed with "_forecast", while +plots generated with the real observation cube are suffixed with "_measured". +Plots showing PDFs with and without priors are infixed with "others". + +- The priors on H0 are: a) Planck b) Reiss c) No prior -- 2D correlation plots with no prior onH0 - -It also collects data to plot a result on H0 for best-fit values of all -other parameters, but currently does not produce that plot """ diff --git a/papers/F/Analysis/py/get_PDFs.py b/papers/F/Analysis/py/plotHWHM.py similarity index 91% rename from papers/F/Analysis/py/get_PDFs.py rename to papers/F/Analysis/py/plotHWHM.py index cb7d7b6b..b4c1baa2 100644 --- a/papers/F/Analysis/py/get_PDFs.py +++ b/papers/F/Analysis/py/plotHWHM.py @@ -1,3 +1,8 @@ +""" +Obtains the lower "half-width half-max" of the log PDFs for the old and new cubes to compare how +much the constraint on F improves after addition of the 2022 FRBs. +""" + import numpy as np import os import zdm @@ -8,27 +13,27 @@ def main(): - # cube_old = "../Real/Cubes/craco_real_old_cube.npz" + # Load cubes cube_old = "../CRACO/Cubes/craco_full_cube.npz" cube = "../Real/Cubes/craco_real_cube.npz" - # old cube + # Get PDFs of the old cube funcs, interp_mins, interp_maxs = getlogPDFs(cube_old) _, _, _, flogF = funcs _, _, _, logF_min = interp_mins _, _, _, logF_max = interp_maxs res = 1e3 - thresh = 1e-3 logFs = np.linspace(logF_min, logF_max, int(res)) probs_old = np.exp(flogF(logFs)) max_prob_old = np.max(probs_old) max_F_old = logFs[np.argmax(probs_old)] + # Find the half-max points sort_idx_old = np.argsort(np.abs(probs_old - (max_prob_old / 2))) half_max_Fs_old = logFs[sort_idx_old[1]] - # new cube + # Get PDFs of the new cube funcs, interp_mins, interp_maxs = getlogPDFs(cube) _, _, _, flogF = funcs _, _, _, logF_min = interp_mins @@ -38,12 +43,10 @@ def main(): max_prob = np.max(probs) max_F = logFs[np.argmax(probs)] + # Find the half-max points sort_idx = np.argsort(np.abs(probs - (max_prob / 2))) half_max_Fs = logFs[sort_idx[3]] - print("debugging", logFs[sort_idx]) - - # Left-sided half width half max print( "Left-sided half-width half-max for old cube: ", np.abs(max_F_old - np.min(half_max_Fs_old)), @@ -88,6 +91,10 @@ def main(): def getlogPDFs(cube): + """ + Returns the log PDFs of the cubes with a flat prior H0. + """ + ######### sets the values of H0 for priors ##### Planck_H0 = 67.4 Planck_sigma = 0.5 diff --git a/papers/F/Analysis/py/plotHubble_wFPrior.py b/papers/F/Analysis/py/plotHubble_wFPrior.py index 90593ba5..e7364697 100644 --- a/papers/F/Analysis/py/plotHubble_wFPrior.py +++ b/papers/F/Analysis/py/plotHubble_wFPrior.py @@ -1,13 +1,14 @@ """ This is a script to produce limit plots for a cube with priors on F -It produces two sets of plots: -- single parameter limits also showing results with: - a) a Gaussian prior - b) No prior +The plots produced with priors on F are stored in folders prefixed with "wF". +Plots generated with the synthetic CRACO cube are suffixed with "_forecast", while +plots generated with the real observation cube are suffixed with "_measured". +Plots showing PDFs with and without priors are infixed with "others". -It also collects data to plot a result on F for best-fit values of all -other parameters, but currently does not produce that plot +- The priors on F are: + a) a Gaussian prior (with 20% error on F) + b) No prior """ @@ -191,5 +192,5 @@ def get_param_values(data, verbose=False): # Real Cube Data -# main("../Real/Cubes/craco_real_cube.npz", "H0_PriorOnF/") -main("../CRACO/Cubes/craco_full_cube.npz", "H0_PriorOnF/") +main("../Real/Cubes/craco_real_cube.npz", "measured/") +main("../CRACO/Cubes/craco_full_cube.npz", "forecast/") diff --git a/papers/F/Tables/results.tex b/papers/F/Tables/results.tex index 1ce6f430..55e42efd 100644 --- a/papers/F/Tables/results.tex +++ b/papers/F/Tables/results.tex @@ -2,10 +2,19 @@ \newcommand{\fctH}{\ensuremath{69.2_{-4.9}^{+5.5}}} \newcommand{\FnoPrior}{\ensuremath{ -0.75 _{-0.25}^{+0.33}}} \newcommand{\fctFnoPrior}{\ensuremath{ -0.57 _{-0.16}^{+0.15}}} +\newcommand{\lmeannoPrior}{\ensuremath{ 2.44 _{-0.06}^{+0.06}}} +\newcommand{\lhostnoPrior}{\ensuremath{ 0.50 _{-0.06}^{+0.08}}} \newcommand{\fctHwPrior}{\ensuremath{67.6_{-3.4}^{+3.5}}} +\newcommand{\fctlmeanwFPrior}{\ensuremath{2.2_{-0.1}^{+0.1}}} +\newcommand{\fctlhostwFPrior}{\ensuremath{0.5_{-0.1}^{+0.1}}} +\newcommand{\HwFPrior}{\ensuremath{80.2_{-7.1}^{+8.5}}} +\newcommand{\lmeanwFPrior}{\ensuremath{2.5_{-0.1}^{+0.1}}} +\newcommand{\lsigmawFPrior}{\ensuremath{0.5_{-0.1}^{+0.1}}} \newcommand{\FwPrior}{\ensuremath{ -0.48 _{-0.18}^{+0.26}}} \newcommand{\FCMB}{\ensuremath{ -0.48 _{-0.18}^{+0.26}}} \newcommand{\FSNe}{\ensuremath{ -0.48 _{-0.18}^{+0.26}}} +\newcommand{\lmeanwHPrior}{\ensuremath{2.4_{-0.1}^{+0.1}}} +\newcommand{\lsigmawHPrior}{\ensuremath{0.5_{-0.1}^{+0.1}}} \newcommand{\fctFwPrior}{\ensuremath{ -0.60 _{-0.1}^{+0.09}}} \newcommand{\fctFCMB}{\ensuremath{ -0.60 _{-0.1}^{+0.09}}} \newcommand{\fctFSNe}{\ensuremath{ -0.48 _{-0.18}^{+0.26}}} diff --git a/papers/F/Tables/tables_F.py b/papers/F/Tables/tables_F.py index bade8eda..e61da5f8 100644 --- a/papers/F/Tables/tables_F.py +++ b/papers/F/Tables/tables_F.py @@ -116,7 +116,8 @@ def mktex_measurements(outfile="results.tex"): "../Analysis/py/wH0_others_forecast/limits_others_$H_0 = 73.04$.dat" ) - real_H0_wFprior_file = "../Analysis/py/wF_H0_PriorOnF/limits.dat" + real_H0_wFprior_file = "../Analysis/py/wF_others_measured/limits.dat" + craco_H0_wFprior_file = "../Analysis/py/wF_others_forecast/limits.dat" def process_limits(lim, precision=1): lower = str(round(float(lim[5:9]), precision)) @@ -126,6 +127,7 @@ def process_limits(lim, precision=1): # No Prior Measurements + ############### H0 with no prior ############## with open(craco_no_prior) as f: craco_lines = f.readlines() @@ -135,7 +137,7 @@ def process_limits(lim, precision=1): craco_H0 = str(round(float(craco_lines[0].split("&")[1]), 1)) craco_H0_lim = process_limits(craco_lines[0].split("&")[-2]) - ############### H0 with no prior ############### + # real_H0 = str(round(float(real_lines[0].split("&")[1]), 1)) real_H0_lim = process_limits(real_lines[0].split("&")[-2]) @@ -165,15 +167,63 @@ def process_limits(lim, precision=1): tbfil.write(real_F_tex) tbfil.write(craco_F_tex) + # Uses real data only # + ############### lmean with no prior ############## + real_lmean = real_lines[1].split("&")[1] + real_lmean_lim = process_limits(real_lines[1].split("&")[-2], 2) + real_lmean_tex = f"\\newcommand{{\\lmeannoPrior}}{{\\ensuremath{{{real_lmean}{real_lmean_lim}}}}} \n" + tbfil.write(real_lmean_tex) + + ############### lsigma with no prior ############## + real_lsigma = real_lines[2].split("&")[1] + real_lsigma_lim = process_limits(real_lines[2].split("&")[-2], 2) + real_lsigma_tex = f"\\newcommand{{\\lhostnoPrior}}{{\\ensuremath{{{real_lsigma}{real_lsigma_lim}}}}} \n" + tbfil.write(real_lsigma_tex) + + ### -------- Measurements with priors --------- ### + ############### H0 with F prior ############### - with open(real_H0_wFprior_file) as f: - real_H0_wFprior_lines = f.readlines() - real_H0_wFprior = str(round(float(real_H0_wFprior_lines[0].split("&")[1]), 1)) - real_H0_wFprior_lim = process_limits(real_H0_wFprior_lines[0].split("&")[-2]) - real_H0_wFprior_tex = f"\\newcommand{{\\fctHwPrior}}{{\\ensuremath{{{real_H0_wFprior}{real_H0_wFprior_lim}}}}} \n" + ## Synthetic Data ## + + with open(craco_H0_wFprior_file) as f: + craco_H0_wFprior_lines = f.readlines() + + craco_H0_wFprior = str(round(float(craco_H0_wFprior_lines[0].split("&")[1]), 1)) + craco_H0_wFprior_lim = process_limits(craco_H0_wFprior_lines[0].split("&")[-2]) + craco_H0_wFprior_tex = f"\\newcommand{{\\fctHwPrior}}{{\\ensuremath{{{craco_H0_wFprior}{craco_H0_wFprior_lim}}}}} \n" + + # craco_lmean_wFprior = str(round(float(craco_H0_wFprior_lines[1].split("&")[1]), 1)) + # craco_lmean_wFprior_lim = process_limits(craco_H0_wFprior_lines[1].split("&")[-2]) + # craco_lmean_wFprior_tex = f"\\newcommand{{\\fctlmeanwFPrior}}{{\\ensuremath{{{craco_lmean_wFprior}{craco_lmean_wFprior_lim}}}}} \n" + + # craco_lsigma_wFprior = str(round(float(craco_H0_wFprior_lines[2].split("&")[1]), 1)) + # craco_lsigma_wFprior_lim = process_limits(craco_H0_wFprior_lines[2].split("&")[-2]) + # craco_lsigma_wFprior_tex = f"\\newcommand{{\\fctlhostwFPrior}}{{\\ensuremath{{{craco_lsigma_wFprior}{craco_lsigma_wFprior_lim}}}}} \n" + + tbfil.write(craco_H0_wFprior_tex) + # tbfil.write(craco_lmean_wFprior_tex) + # tbfil.write(craco_lsigma_wFprior_tex) - tbfil.write(real_H0_wFprior_tex) + ## Real Data -- This doesn't make sense lol## + # with open(real_H0_wFprior_file) as f: + # real_H0_wFprior_lines = f.readlines() + + # real_H0_wFprior = str(round(float(real_H0_wFprior_lines[0].split("&")[1]), 1)) + # real_H0_wFprior_lim = process_limits(real_H0_wFprior_lines[0].split("&")[-2]) + # real_H0_wFprior_tex = f"\\newcommand{{\\HwFPrior}}{{\\ensuremath{{{real_H0_wFprior}{real_H0_wFprior_lim}}}}} \n" + + # real_lmean_wFprior = str(round(float(real_H0_wFprior_lines[1].split("&")[1]), 1)) + # real_lmean_wFprior_lim = process_limits(real_H0_wFprior_lines[1].split("&")[-2]) + # real_lmean_wFprior_tex = f"\\newcommand{{\\lmeanwFPrior}}{{\\ensuremath{{{real_lmean_wFprior}{real_lmean_wFprior_lim}}}}} \n" + + # real_lsigma_wFprior = str(round(float(real_H0_wFprior_lines[2].split("&")[1]), 1)) + # real_lsigma_wFprior_lim = process_limits(real_H0_wFprior_lines[2].split("&")[-2]) + # real_lsigma_wFprior_tex = f"\\newcommand{{\\lsigmawFPrior}}{{\\ensuremath{{{real_lsigma_wFprior}{real_lsigma_wFprior_lim}}}}} \n" + + # tbfil.write(real_H0_wFprior_tex) + # tbfil.write(real_lmean_wFprior_tex) + # tbfil.write(real_lsigma_wFprior_tex) ############### F with H0 prior ############### # Measurements @@ -183,6 +233,15 @@ def process_limits(lim, precision=1): real_F_wH0prior = real_F_wH0prior_lines[-1].split("&")[1] real_F_wH0prior_lim = process_limits(real_F_wH0prior_lines[-1].split("&")[-2], 2) + real_F_wH0prior_tex = f"\\newcommand{{\\FwHPrior}}{{\\ensuremath{{{real_lsigma_wFprior}{real_lsigma_wFprior_lim}}}}} \n" + + real_lmean_wH0prior = str(round(float(real_F_wH0prior_lines[1].split("&")[1]), 1)) + real_lmean_wH0prior_lim = process_limits(real_F_wH0prior_lines[1].split("&")[-2]) + real_lmean_wH0prior_tex = f"\\newcommand{{\\lmeanwHPrior}}{{\\ensuremath{{{real_lmean_wH0prior}{real_lmean_wH0prior_lim}}}}} \n" + + real_lsigma_wH0prior = str(round(float(real_F_wH0prior_lines[2].split("&")[1]), 1)) + real_lsigma_wH0prior_lim = process_limits(real_F_wH0prior_lines[2].split("&")[-2]) + real_lsigma_wH0prior_tex = f"\\newcommand{{\\lsigmawHPrior}}{{\\ensuremath{{{real_lsigma_wH0prior}{real_lsigma_wH0prior_lim}}}}} \n" # CMB with open(real_F_CMB_file) as f: @@ -210,6 +269,9 @@ def process_limits(lim, precision=1): tbfil.write(real_F_CMB_tex) tbfil.write(real_F_SNe_tex) + tbfil.write(real_lmean_wH0prior_tex) + tbfil.write(real_lsigma_wH0prior_tex) + # Forecasts with open(craco_F_wH0prior_file) as f: craco_F_wH0prior_lines = f.readlines() diff --git a/zdm/analyze_cube.py b/zdm/analyze_cube.py index d6adac8f..b4adcc58 100644 --- a/zdm/analyze_cube.py +++ b/zdm/analyze_cube.py @@ -120,8 +120,6 @@ def slurp_cube(input_file:str, prefix:str, outfile:str, pz=pz_cube, ) - # embed(header="line 129") - # Save the parameter values too for name in PARAMS[:-1]: @@ -550,8 +548,9 @@ def get_2D_bayesian_data(lls: np.ndarray, plls: np.ndarray = None, pklfile=None) lls = origlls[tuple(big_slice)].flatten() # ignores all values of 0, which is what missing data is - ignore = np.where(lls == 0.0)[0] - lls[ignore] = -99999 + # ignore = np.where(lls == 0.0)[0] + # lls[ignore] = -99999 + lls[np.isnan(lls)] = -99999 try: themax = np.nanmax(lls) diff --git a/zdm/craco/MC_F/Surveys/F_0.32_survey.dat b/zdm/craco/MC_F/Surveys/F_0.32_survey.dat index a9ab2b88..aad8c47e 100644 --- a/zdm/craco/MC_F/Surveys/F_0.32_survey.dat +++ b/zdm/craco/MC_F/Surveys/F_0.32_survey.dat @@ -3,6 +3,7 @@ FRES 1 #MHz DIAM 12 NBEAMS 36 BEAM lat50_log #prefix of beam file +NBINS 5 FBAR 1320 TRES 1.7 #ms SNRTHRESH 9.5 diff --git a/zdm/craco/MC_F/Surveys/F_0.32_survey.ecsv b/zdm/craco/MC_F/Surveys/F_0.32_survey.ecsv index bcc05a2a..0a7b80c4 100644 --- a/zdm/craco/MC_F/Surveys/F_0.32_survey.ecsv +++ b/zdm/craco/MC_F/Surveys/F_0.32_survey.ecsv @@ -19,7 +19,7 @@ # - {name: Z, datatype: float64} # meta: !!omap # - {survey_data: "{\n \"observing\": {\n \"NORM_FRB\": 1000,\n \"TOBS\": 96.65\n },\n \"telescope\": {\n \ -# \ \"BEAM\": \"lat50_log\",\n \"DIAM\": 12.0,\n \"NBEAMS\": 36\n }\n}"} +# \ \"BEAM\": \"lat50_log\",\n \"DIAM\": 12.0,\n \"NBEAMS\": 36,\n \"NBINS\": 5\n }\n}"} # schema: astropy-2.0 TNS BW DM DMG FBAR FRES Gb Gl SNR SNRTHRESH THRESH TRES WIDTH XDec XRA Z 0 288.0 550.1 35.0 1320.0 1.0 "" "" 25.5 9.5 0.99 1.7 2.0 "" "" 0.313 diff --git a/zdm/data/Surveys/CRAFT_ICS.ecsv b/zdm/data/Surveys/CRAFT_ICS.ecsv index 30a0e2c8..4c75d5cd 100644 --- a/zdm/data/Surveys/CRAFT_ICS.ecsv +++ b/zdm/data/Surveys/CRAFT_ICS.ecsv @@ -35,3 +35,6 @@ TNS BW DM DMG FBAR FRES Gb Gl SNR SNRTHRESH THRESH TRES WIDTH XDec XRA Z 20210407E 336.0 1785.3 154.0 1271.5 1.0 "" "" 19.1 9.0 4.4 1.182 8.0 27:03:30.24 05:14:36.202 -1.0 20210912A 336.0 1234.5 30.9 1271.5 1.0 "" "" 31.7 9.0 4.4 1.182 5.5 -30:29:33.1 23:24:40.3 -1.0 20211127I 336.0 234.83 42.5 1271.5 1.0 "" "" 37.9 9.0 4.4 1.182 1.41 -18:49:28.4 13:19:09.5 0.046946 +20220531A 336.0 727.0 70.0 1271.5 1.0 "" "" 9.7 9.0 4.4 1.182 11.0 -60:17:48.2 "" -1.0 +20220610A 336.0 1458.1 31.0 1271.5 1.0 "" "" 29.8 9.0 4.4 1.182 5.6 -33:30:39.02 "" 1.016 +20220918A 336.0 656.8 40.7 1271.5 1.0 "" "" 26.4 9.0 4.4 1.182 7.1 -70:47:05.9 "" -1.0 \ No newline at end of file diff --git a/zdm/data/Surveys/CRAFT_ICS_1632.ecsv b/zdm/data/Surveys/CRAFT_ICS_1632.ecsv index 485c9407..96d59484 100644 --- a/zdm/data/Surveys/CRAFT_ICS_1632.ecsv +++ b/zdm/data/Surveys/CRAFT_ICS_1632.ecsv @@ -24,3 +24,5 @@ # schema: astropy-2.0 TNS BW DM DMG FBAR FRES Gb Gl SNR SNRTHRESH THRESH TRES WIDTH XC XDec XRA Z 20211212A 336.0 206.0 27.1 1632.5 1.0 "" "" 12.8 9.0 4.4 1.182 2.7 closepack36/45/0.9 01:40:36.8 10:30:40.7 0.0715 +20220105A 336.0 583.0 22.0 1632.5 1.0 "" "" 9.8 9.0 4.4 1.182 2.0 "" 13:54:51.4 0.2785 +20221106A 336.0 344.0 34.8 1631.5 1.0 "" "" 35.1 9.0 4.4 1.182 5.7 "" 03:46:38.1 -1.0 \ No newline at end of file diff --git a/zdm/data/Surveys/CRAFT_ICS_892.ecsv b/zdm/data/Surveys/CRAFT_ICS_892.ecsv index 632a4bf8..a4c3cded 100644 --- a/zdm/data/Surveys/CRAFT_ICS_892.ecsv +++ b/zdm/data/Surveys/CRAFT_ICS_892.ecsv @@ -31,3 +31,4 @@ TNS BW DM DMG FBAR FRES Gb Gl SNR SNRTHRESH THRESH TRES WIDTH XC XDec XRA Z 20210807D 336.0 251.9 121.2 920.5 1.0 "" "" 47.1 9.0 4.4 1.182 10.0 square6x6/45/0.9 -00:45:44.5 19:56:53.144 0.12969 20210809C 336.0 651.5 190.1 920.5 1.0 "" "" 16.8 9.0 4.4 1.182 14.2 square6x6/45/0.9 01:19:43.5 18:04:37.7 -1.0 20211203C 336.0 636.2 63.4 920.5 1.0 "" "" 14.2 9.0 4.4 1.182 9.6 closepack36/45/0.9 -31:22:04.0 13:37:52.8 0.34386 +20220725A 336.0 290.4 30.7 920.5 1.0 "" "" 12.7 9.0 4.4 1.182 4.1 "" 23:33:32.1 0.1926 \ No newline at end of file diff --git a/zdm/survey.py b/zdm/survey.py index 697275ac..874d7b0c 100644 --- a/zdm/survey.py +++ b/zdm/survey.py @@ -171,7 +171,7 @@ def process_survey_file(self,filename:str, NFRB:int=None, if NFRB is not None: # Take the first set - ensures we do not overrun the total number of FRBs - if self.NFRB > NFRB+iFRB: + if self.NFRB < NFRB+iFRB: raise ValueError("Cannot return sufficient FRBs, did you mean NFRB=None?") themax = min(NFRB+iFRB,self.NFRB) self.frblist=self.frblist[iFRB:themax] @@ -528,7 +528,7 @@ def process_survey_file(self,filename:str, self.NFRB=len(self.frbs) else: self.NFRB=min(len(self.frbs), NFRB) - if self.NFRB > NFRB+iFRB: + if self.NFRB < NFRB+iFRB: raise ValueError("Cannot return sufficient FRBs, did you mean NFRB=None?") # Not sure the following linematters given the Error above themax = max(NFRB+iFRB,self.NFRB) @@ -1123,7 +1123,7 @@ def refactor_old_survey_file(survey_name:str, outfile:str, separators=(',', ': ')) # Write me - frbs.write(outfile, overwrite=clobber) + frbs.write(outfile, overwrite=clobber, format='ascii.ecsv') print(f"Wrote: {outfile}") def vet_frb_table(frb_tbl:pandas.DataFrame, From 2fea927d04600fd634bc88820b0f4595a3de252f Mon Sep 17 00:00:00 2001 From: profxj Date: Fri, 14 Jul 2023 05:31:09 -0700 Subject: [PATCH 101/104] test fixes --- .github/workflows/ci_tests.yml | 2 +- zdm/data/Surveys/CRAFT_ICS_892.ecsv | 3 +-- zdm/real_loading.py | 11 ++++++++--- zdm/survey.py | 3 ++- zdm/tests/test_cube.py | 18 ++++++++++++------ 5 files changed, 24 insertions(+), 13 deletions(-) diff --git a/.github/workflows/ci_tests.yml b/.github/workflows/ci_tests.yml index 4f12b4a0..0bd91a89 100644 --- a/.github/workflows/ci_tests.yml +++ b/.github/workflows/ci_tests.yml @@ -16,7 +16,7 @@ jobs: strategy: matrix: os: [ubuntu-latest] - python: [3.8, 3.9] + python: [3.9,3.10] toxenv: [test, test-alldeps, test-astropydev] steps: - name: Check out repository diff --git a/zdm/data/Surveys/CRAFT_ICS_892.ecsv b/zdm/data/Surveys/CRAFT_ICS_892.ecsv index a4c3cded..26874f13 100644 --- a/zdm/data/Surveys/CRAFT_ICS_892.ecsv +++ b/zdm/data/Surveys/CRAFT_ICS_892.ecsv @@ -30,5 +30,4 @@ TNS BW DM DMG FBAR FRES Gb Gl SNR SNRTHRESH THRESH TRES WIDTH XC XDec XRA Z 20210320C 336.0 384.8 42.2 864.5 1.0 "" "" 15.3 9.0 4.4 1.728 5.4 square_6x6/45/1.05 -16:09:05.1 13:37:50.08605 0.28 20210807D 336.0 251.9 121.2 920.5 1.0 "" "" 47.1 9.0 4.4 1.182 10.0 square6x6/45/0.9 -00:45:44.5 19:56:53.144 0.12969 20210809C 336.0 651.5 190.1 920.5 1.0 "" "" 16.8 9.0 4.4 1.182 14.2 square6x6/45/0.9 01:19:43.5 18:04:37.7 -1.0 -20211203C 336.0 636.2 63.4 920.5 1.0 "" "" 14.2 9.0 4.4 1.182 9.6 closepack36/45/0.9 -31:22:04.0 13:37:52.8 0.34386 -20220725A 336.0 290.4 30.7 920.5 1.0 "" "" 12.7 9.0 4.4 1.182 4.1 "" 23:33:32.1 0.1926 \ No newline at end of file +20211203C 336.0 636.2 63.4 920.5 1.0 "" "" 14.2 9.0 4.4 1.182 9.6 closepack36/45/0.9 -31:22:04.0 13:37:52.8 0.34386 \ No newline at end of file diff --git a/zdm/real_loading.py b/zdm/real_loading.py index acedd632..6a2b5078 100644 --- a/zdm/real_loading.py +++ b/zdm/real_loading.py @@ -84,8 +84,10 @@ def set_state(alpha_method=1, cosmo=Planck18): return state -def surveys_and_grids(init_state=None, alpha_method=1, add_20220610A=False): - """ Load up a survey and grid for a CRACO mock dataset +def surveys_and_grids(init_state=None, alpha_method=1, + survey_names=None, + add_20220610A=False): + """ Load up a survey and grid for a real dataset Args: init_state (State, optional): @@ -93,6 +95,8 @@ def surveys_and_grids(init_state=None, alpha_method=1, add_20220610A=False): survey_name (str, optional): Defaults to 'CRAFT/CRACO_1_5000'. state_dict (dict, optional): Used to init state instead of alpha_method, lum_func parameters + survey_names (list, optional): + List of surveys to load add_20220610A (bool, optional): Include this FRB (a bit of a hack) @@ -118,7 +122,8 @@ def surveys_and_grids(init_state=None, alpha_method=1, add_20220610A=False): datdir=resource_filename('zdm', 'GridData')) ############## Initialise surveys ############## - survey_names = ['CRAFT/FE', + if survey_names is None: + survey_names = ['CRAFT/FE', 'private_CRAFT_ICS_1632', 'private_CRAFT_ICS_892', 'private_CRAFT_ICS_1272', diff --git a/zdm/survey.py b/zdm/survey.py index 874d7b0c..36788194 100644 --- a/zdm/survey.py +++ b/zdm/survey.py @@ -982,7 +982,6 @@ def load_survey(survey_name:str, state:parameters.State, Returns: Survey: instance of the class """ - print(f"Loading survey: {survey_name}") if sdir is None: sdir = os.path.join( resource_filename('zdm', 'data'), 'Surveys') @@ -1017,6 +1016,8 @@ def load_survey(survey_name:str, state:parameters.State, else: dfile += '.ecsv' + print(f"Loading survey: {survey_name} from {dfile}") + # Do it if original: srvy=OldSurvey() diff --git a/zdm/tests/test_cube.py b/zdm/tests/test_cube.py index 10da3bc1..f8a03c01 100644 --- a/zdm/tests/test_cube.py +++ b/zdm/tests/test_cube.py @@ -9,7 +9,7 @@ import pandas from zdm import iteration as it -from zdm.craco import loading +from zdm import real_loading from zdm import io from zdm.tests import tstutils @@ -30,16 +30,22 @@ def test_cube_run(): # Initialise survey and grid # For this purporse, we only need two different surveys #names=['CRAFT/FE','CRAFT/ICS','CRAFT/ICS892','CRAFT/ICS1632','PKS/Mb'] - names=['CRAFT/FE','CRAFT/ICS','CRAFT/ICS892','PKS/Mb'] - sdir = os.path.join(resource_filename('zdm', 'data'), 'Surveys') - surveys=[] - grids=[] + names=['CRAFT/FE','CRAFT/ICS','CRAFT/ICS892', + 'PKS/Mb'] + #sdir = os.path.join(resource_filename('zdm', 'data'), 'Surveys') + #surveys=[] + #grids=[] + + ''' + # We should be using real_loading for name in names: - # We should be using real_loading s,g = loading.survey_and_grid( survey_name=name,NFRB=None,sdir=sdir) # should be equal to actual number of FRBs, but for this purpose it doesn't matter surveys.append(s) grids.append(g) + ''' + surveys, grids = real_loading.surveys_and_grids( + survey_names=names) ### gets cube files From 4dc68cd94deb7cde256bad53713f4a5dcf36a7cd Mon Sep 17 00:00:00 2001 From: profxj Date: Fri, 14 Jul 2023 05:38:34 -0700 Subject: [PATCH 102/104] quotes --- .github/workflows/ci_tests.yml | 2 +- zdm/tests/test_cube.py | 32 +------------------------------- 2 files changed, 2 insertions(+), 32 deletions(-) diff --git a/.github/workflows/ci_tests.yml b/.github/workflows/ci_tests.yml index 0bd91a89..705fc2e6 100644 --- a/.github/workflows/ci_tests.yml +++ b/.github/workflows/ci_tests.yml @@ -16,7 +16,7 @@ jobs: strategy: matrix: os: [ubuntu-latest] - python: [3.9,3.10] + python: ['3.9','3.10'] toxenv: [test, test-alldeps, test-astropydev] steps: - name: Check out repository diff --git a/zdm/tests/test_cube.py b/zdm/tests/test_cube.py index f8a03c01..3ea48c0e 100644 --- a/zdm/tests/test_cube.py +++ b/zdm/tests/test_cube.py @@ -113,34 +113,4 @@ def test_cube_run(): zdm_v1= ds.p_zgDM + ds.p_DM zdm_v2= ds.p_DMgz + ds.p_z assert check_accuracy(zdm_v1,zdm_v2) - - ''' - # now check it has the right dimensions - with open(outfile, 'r') as infile: - lines=infile.readlines() - assert len(lines)==howmany+1 - for i,line in enumerate(lines): - if i==0: - colmns = line.split(',') - continue - words=line.split(',') - - embed(header='106 of test_cube') - # three ways of calculating lltot - lltot_v0=float(words[-8]) - lltot_v1=0 - for j in np.arange(ns): - lltot_v1 += float(words[9+5*j]) - lltot_v2=float(words[-5])+float(words[-6])+float(words[-7]) - - # three ways of calculating p(z,DM) - zdm_v1=float(words[-3])+float(words[-4]) - zdm_v2=float(words[-1])+float(words[-2]) - - assert check_accuracy(zdm_v1,zdm_v2) - - assert check_accuracy(lltot_v0,lltot_v1) - assert check_accuracy(lltot_v0,lltot_v2) - ''' - -#test_cube_run() \ No newline at end of file + \ No newline at end of file From 0e528c86c24387791cd3b3c038959c89765a4209 Mon Sep 17 00:00:00 2001 From: profxj Date: Fri, 14 Jul 2023 05:46:39 -0700 Subject: [PATCH 103/104] input nz, ndm --- zdm/real_loading.py | 10 ++++++++-- zdm/tests/test_cube.py | 7 +++++-- 2 files changed, 13 insertions(+), 4 deletions(-) diff --git a/zdm/real_loading.py b/zdm/real_loading.py index 6a2b5078..f067092d 100644 --- a/zdm/real_loading.py +++ b/zdm/real_loading.py @@ -86,7 +86,8 @@ def set_state(alpha_method=1, cosmo=Planck18): def surveys_and_grids(init_state=None, alpha_method=1, survey_names=None, - add_20220610A=False): + add_20220610A=False, + nz:int=2000, ndm:int=5600): """ Load up a survey and grid for a real dataset Args: @@ -99,6 +100,10 @@ def surveys_and_grids(init_state=None, alpha_method=1, List of surveys to load add_20220610A (bool, optional): Include this FRB (a bit of a hack) + nz (int, optional): + Number of redshift bins + ndm (int, optional): + Number of DM bins Raises: IOError: [description] @@ -118,7 +123,8 @@ def surveys_and_grids(init_state=None, alpha_method=1, # get the grid of p(DM|z) zDMgrid, zvals,dmvals = misc_functions.get_zdm_grid( - state, new=True, plot=False, method='analytic', nz=2000, ndm=5600, + state, new=True, plot=False, method='analytic', + nz=nz, ndm=ndm, datdir=resource_filename('zdm', 'GridData')) ############## Initialise surveys ############## diff --git a/zdm/tests/test_cube.py b/zdm/tests/test_cube.py index 3ea48c0e..eb5470f4 100644 --- a/zdm/tests/test_cube.py +++ b/zdm/tests/test_cube.py @@ -45,7 +45,8 @@ def test_cube_run(): grids.append(g) ''' surveys, grids = real_loading.surveys_and_grids( - survey_names=names) + survey_names=names, + nz=500, ndm=1400) # Small number to keep this cheap ### gets cube files @@ -113,4 +114,6 @@ def test_cube_run(): zdm_v1= ds.p_zgDM + ds.p_DM zdm_v2= ds.p_DMgz + ds.p_z assert check_accuracy(zdm_v1,zdm_v2) - \ No newline at end of file + + +#test_cube_run() \ No newline at end of file From 04c02822d90cc9f94755bc8e7917204d7bfc92cf Mon Sep 17 00:00:00 2001 From: Jay Baptista Date: Thu, 20 Jul 2023 18:53:44 -1000 Subject: [PATCH 104/104] address Clancy's comments for PR --- .gitignore | 5 - .../CRACO/Cloud/deprecated/OutputH0F.tar.gz | Bin 213618 -> 0 bytes .../Real/Cloud/Output_low_res/explore.ipynb | 105 ------------------ .../F/Analysis/Real/Cloud/run_craco_real.py | 21 +++- .../Real/Cloud/run_real_craco_block.py | 71 ------------ .../Cloud/yamls/nautilus_real_cube_b1.yaml | 2 +- zdm/data/Surveys/CRAFT_ICS_1632.ecsv | 4 +- zdm/data/Surveys/CRAFT_ICS_892.ecsv | 2 +- zdm/real_loading.py | 6 +- 9 files changed, 27 insertions(+), 189 deletions(-) delete mode 100644 papers/F/Analysis/CRACO/Cloud/deprecated/OutputH0F.tar.gz delete mode 100644 papers/F/Analysis/Real/Cloud/Output_low_res/explore.ipynb delete mode 100644 papers/F/Analysis/Real/Cloud/run_real_craco_block.py diff --git a/.gitignore b/.gitignore index 5563859b..288aab86 100644 --- a/.gitignore +++ b/.gitignore @@ -170,8 +170,3 @@ zdm/untitled10.py *.DS_Store papers/H0_I/Analysis/Real/minicube_real.src - - -zdm/data/Surveys/private_CRAFT_ICS_892.ecsv -zdm/data/Surveys/private_CRAFT_ICS_1272.ecsv -zdm/data/Surveys/private_CRAFT_ICS_1632.ecsv diff --git 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z;a7mJU3A0;uG`RX3SlNw0Fseyz<0*auMK{Rrys$@<6%cyRut$K1GFVVTWaYmLUgua z0=AKnv6^ayC+Z{^fGIcZLv+HPR!Raof2d>|AiyXM#C@TB@%k1m+)UYBY|Yu_rKG|X zs4(PbJDMLHD$@4%qG-K1l8{w8l0OyzqBXdrukci>NKt%*AU(e^I|}W0BNY@^TED(M z#ER5x3+iKiXiOsYRa Upa1^+KmX@{0Vn=NLI5xf08ol5%K!iX diff --git a/papers/F/Analysis/Real/Cloud/Output_low_res/explore.ipynb b/papers/F/Analysis/Real/Cloud/Output_low_res/explore.ipynb deleted file mode 100644 index 5f2da120..00000000 --- a/papers/F/Analysis/Real/Cloud/Output_low_res/explore.ipynb +++ /dev/null @@ -1,105 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from astropy.table import Table\n", - "import numpy as np\n", - "import pandas as pd" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "vals = []\n", - "for k in np.arange(1, 42):\n", - " tab = pd.read_csv(f\"craco_real{k}.csv\")\n", - " vals.append(sum(np.isnan(tab.lls)))" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ True, True, True, True, False, False, False, False, False,\n", - " False, False, False, False, False, False, False, False, False,\n", - " False, False, False, False, False, False, False, False, False,\n", - " False, False, False, False, False, False, False, False, False,\n", - " False, False, False, False, False])" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.array(vals) > 0" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "600" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.sum(vals)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "base", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "9731a3b7ebb41545a410367c249afbc4842fd5740da82f1bd29e6ac1d7253e6e" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/papers/F/Analysis/Real/Cloud/run_craco_real.py b/papers/F/Analysis/Real/Cloud/run_craco_real.py index 9a80aa23..fbbb2a47 100644 --- a/papers/F/Analysis/Real/Cloud/run_craco_real.py +++ b/papers/F/Analysis/Real/Cloud/run_craco_real.py @@ -42,8 +42,18 @@ def main( if int(ntotal / total_ncpu) != nper_cpu: raise IOError(f"Ncpu={total_ncpu} must divide evenly into ntotal={ntotal}") + start = pargs.start + end = pargs.end + commands = [] - for kk in range(pargs.ncpu): + + nums = range(pargs.ncpu) + + # Restrict to subset of CPUs if specified + if (start > 0) and (end > 0): + nums = np.arange(start-1, end, dtype="int") + + for kk in nums: line = [] # Which CPU is running out of the total? iCPU = (batch - 1) * pargs.ncpu + kk @@ -102,6 +112,15 @@ def parse_option(): parser.add_argument( "-b", "--batch", type=int, default=1, required=False, help="Batch number" ) + + # Optional restriction to subset of cube + parser.add_argument( + "-s", "--start", type=int, default=0, required=False, help="csv to start on", + ) + parser.add_argument( + "-e", "--end", type=int, default=0, required=False, help="csv to end on (inclusive)", + ) + args = parser.parse_args() return args diff --git a/papers/F/Analysis/Real/Cloud/run_real_craco_block.py b/papers/F/Analysis/Real/Cloud/run_real_craco_block.py deleted file mode 100644 index 4537f940..00000000 --- a/papers/F/Analysis/Real/Cloud/run_real_craco_block.py +++ /dev/null @@ -1,71 +0,0 @@ -""" -This script generates the `.csv` files for the likelihood cube using real FRB observations (see Baptista+23) -This script is modified to generate specific cube `.csv` files between a range of numbers that correspond to the indices along the H_0 dimension in the cube. -""" - -# Running this command: python ../py/build_real_cube.py -n 1 -m 3000 -o Output/craco_real1.csv --clobber -p ../Cubes/craco_real_cube.json - -import argparse -import numpy as np -import subprocess - - -def main(pargs): - - print(f"Running batch from CSVs {pargs.start} to {pargs.end}") - start = pargs.start - end = pargs.end - nums = np.arange(start, end + 1, dtype="int") - - commands = [] - - for number in nums: - - line = [ - "python", - "../py/build_real_cube.py", - "-n", - f"{number}", - "-m", - "3000", - "-o", - f"Output/craco_real{number}.csv", - "--clobber", - "-p", - f"../Cubes/craco_real_cube.json", - ] - commands.append(line) - - processes = [] - - for command in commands: - print(f"Running this command: {' '.join(command)}") - pw = subprocess.Popen(command) - processes.append(pw) - - for pw in processes: - exit_code = pw.wait() - print(exit_code) - - print("All done!") - - -def parse_option(): - # test for command-line arguments here - parser = argparse.ArgumentParser() - parser.add_argument( - "-s", "--start", type=int, required=True, help="csv to start on", - ) - parser.add_argument( - "-e", "--end", type=int, required=False, help="csv to end on (inclusive)", - ) - - args = parser.parse_args() - - return args - - -if __name__ == "__main__": - # get the argument of training. - pargs = parse_option() - main(pargs) diff --git a/papers/F/Analysis/Real/Cloud/yamls/nautilus_real_cube_b1.yaml b/papers/F/Analysis/Real/Cloud/yamls/nautilus_real_cube_b1.yaml index 5bad8793..66e3d357 100644 --- a/papers/F/Analysis/Real/Cloud/yamls/nautilus_real_cube_b1.yaml +++ b/papers/F/Analysis/Real/Cloud/yamls/nautilus_real_cube_b1.yaml @@ -52,7 +52,7 @@ spec: cd ../../..; cd papers/F/Analysis/Real/Cloud; mkdir Output; - python run_real_craco_block.py -s 1 -e 5; + python run_craco_real.py -t 41 -n 41 -s 1 -e 5; aws --endpoint http://rook-ceph-rgw-nautiluss3.rook s3 cp Output s3://zdm/Cubes/F/real/ --recursive --force; env: - name: "ENDPOINT_URL" diff --git a/zdm/data/Surveys/CRAFT_ICS_1632.ecsv b/zdm/data/Surveys/CRAFT_ICS_1632.ecsv index 96d59484..1d7574e3 100644 --- a/zdm/data/Surveys/CRAFT_ICS_1632.ecsv +++ b/zdm/data/Surveys/CRAFT_ICS_1632.ecsv @@ -24,5 +24,5 @@ # schema: astropy-2.0 TNS BW DM DMG FBAR FRES Gb Gl SNR SNRTHRESH THRESH TRES WIDTH XC XDec XRA Z 20211212A 336.0 206.0 27.1 1632.5 1.0 "" "" 12.8 9.0 4.4 1.182 2.7 closepack36/45/0.9 01:40:36.8 10:30:40.7 0.0715 -20220105A 336.0 583.0 22.0 1632.5 1.0 "" "" 9.8 9.0 4.4 1.182 2.0 "" 13:54:51.4 0.2785 -20221106A 336.0 344.0 34.8 1631.5 1.0 "" "" 35.1 9.0 4.4 1.182 5.7 "" 03:46:38.1 -1.0 \ No newline at end of file +20220105A 336.0 583.0 22.0 1632.5 1.0 "" "" 9.8 9.0 4.4 1.182 2.0 "" 22:29:19.7 13:54:51.4 0.2785 +20221106A 336.0 344.0 34.8 1631.5 1.0 "" "" 35.1 9.0 4.4 1.182 5.7 "" -25:39:44.9 03:46:38.1 -1.0 \ No newline at end of file diff --git a/zdm/data/Surveys/CRAFT_ICS_892.ecsv b/zdm/data/Surveys/CRAFT_ICS_892.ecsv index a4c3cded..ff8be05a 100644 --- a/zdm/data/Surveys/CRAFT_ICS_892.ecsv +++ b/zdm/data/Surveys/CRAFT_ICS_892.ecsv @@ -31,4 +31,4 @@ TNS BW DM DMG FBAR FRES Gb Gl SNR SNRTHRESH THRESH TRES WIDTH XC XDec XRA Z 20210807D 336.0 251.9 121.2 920.5 1.0 "" "" 47.1 9.0 4.4 1.182 10.0 square6x6/45/0.9 -00:45:44.5 19:56:53.144 0.12969 20210809C 336.0 651.5 190.1 920.5 1.0 "" "" 16.8 9.0 4.4 1.182 14.2 square6x6/45/0.9 01:19:43.5 18:04:37.7 -1.0 20211203C 336.0 636.2 63.4 920.5 1.0 "" "" 14.2 9.0 4.4 1.182 9.6 closepack36/45/0.9 -31:22:04.0 13:37:52.8 0.34386 -20220725A 336.0 290.4 30.7 920.5 1.0 "" "" 12.7 9.0 4.4 1.182 4.1 "" 23:33:32.1 0.1926 \ No newline at end of file +20220725A 336.0 290.4 30.7 920.5 1.0 "" "" 12.7 9.0 4.4 1.182 4.1 "" "" 23:33:32.1 0.1926 \ No newline at end of file diff --git a/zdm/real_loading.py b/zdm/real_loading.py index acedd632..802608b5 100644 --- a/zdm/real_loading.py +++ b/zdm/real_loading.py @@ -119,9 +119,9 @@ def surveys_and_grids(init_state=None, alpha_method=1, add_20220610A=False): ############## Initialise surveys ############## survey_names = ['CRAFT/FE', - 'private_CRAFT_ICS_1632', - 'private_CRAFT_ICS_892', - 'private_CRAFT_ICS_1272', + 'CRAFT_ICS_1632', + 'CRAFT_ICS_892', + 'CRAFT_ICS_1272', 'PKS/Mb'] if add_20220610A: survey_names[3] = 'CRAFT_ICS_w_220610'